Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services...Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.展开更多
Crop leaf area index(LAI)and biomass are two major biophysical parameters to measure crop growth and health condition.Measuring LAI and biomass in field experiments is a destructive method.Therefore,we focused on the ...Crop leaf area index(LAI)and biomass are two major biophysical parameters to measure crop growth and health condition.Measuring LAI and biomass in field experiments is a destructive method.Therefore,we focused on the application of unmanned aerial vehicles(UAVs)in agriculture,which is a cost and labor-efficientmethod.Hence,UAV-captured multispectral images were applied to monitor crop growth,identify plant bio-physical conditions,and so on.In this study,we monitored soybean crops using UAV and field experiments.This experiment was conducted at theMAFES(Mississippi Agricultural and Forestry Experiment Station)Pontotoc Ridge-Flatwoods Branch Experiment Station.It followed a randomized block design with five cover crops:Cereal Rye,Vetch,Wheat,MC:mixed Mustard and Cereal Rye,and native vegetation.Planting was made in the fall,and three fertilizer treatments were applied:Synthetic Fertilizer,Poultry Litter,and none,applied before planting the soybean,in a full factorial combination.We monitored soybean reproductive phases at R3(initial pod development),R5(initial seed development),R6(full seed development),and R7(initial maturity)and used UAV multispectral remote sensing for soybean LAI and biomass estimations.The major goal of this study was to assess LAI and biomass estimations from UAV multispectral images in the reproductive stages when the development of leaves and biomass was stabilized.Wemade about fourteen vegetation indices(VIs)fromUAVmultispectral images at these stages to estimate LAI and biomass.Wemodeled LAI and biomass based on these remotely sensed VIs and ground-truth measurements usingmachine learning methods,including linear regression,Random Forest(RF),and support vector regression(SVR).Thereafter,the models were applied to estimate LAI and biomass.According to the model results,LAI was better estimated at the R6 stage and biomass at the R3 stage.Compared to the other models,the RF models showed better estimation,i.e.,an R^(2) of about 0.58–0.68 with an RMSE(rootmean square error)of 0.52–0.60(m^(2)/m^(2))for the LAI and about 0.44–0.64 for R^(2) and 21–26(g dry weight/5 plants)for RMSE of biomass estimation.We performed a leave-one-out cross-validation.Based on cross-validatedmodels with field experiments,we also found that the R6 stage was the best for estimating LAI,and the R3 stage for estimating crop biomass.The cross-validated RF model showed the estimation ability with an R^(2) about 0.25–0.44 and RMSE of 0.65–0.85(m^(2)/m^(2))for LAI estimation;and R^(2) about 0.1–0.31 and an RMSE of about 28–35(g dry weight/5 plants)for crop biomass estimation.This result will be helpful to promote the use of non-destructive remote sensing methods to determine the crop LAI and biomass status,which may bring more efficient crop production and management.展开更多
Most multispectral compatible infrared camouflage devices primarily focus on achieving low emissivity but neglect environmental emissivity matching when environmental emissivity exceeds that of the devices,this create...Most multispectral compatible infrared camouflage devices primarily focus on achieving low emissivity but neglect environmental emissivity matching when environmental emissivity exceeds that of the devices,this creates a"low-emissivity exposure"risk.To address this issue,we develop a tunable multispectral compatible infrared camouflage device using phase change material In3SbTe2(IST).Simulation and experimental results demonstrate that in both the amorphous(aIST)and crystalline(cIST)states,the device achieves simulated plant infrared camouflage and ultra-low emissivity infrared camouflage within the atmospheric window bands(3–5μm and 8–14μm).To address thermal management,it utilizes two non-atmospheric window bands(2.5–3μm and 5–8μm)for heat dissipation.Additionally,laser stealth is realized at three specific wavelengths(1.064μm,1.55μm,and 10.6μm).In the visible spectrum,high absorptivity enables effective visible light camouflage.Adjusting the geometric parameters of top layer structure enables color variation.This work not only highlights potential applications in reversible switching,reconfigurable imaging,and dynamic coding using IST but also offers an effective strategy to counter multispectral detection technology.展开更多
Multispectral imaging plays a crucial role in simultaneously capturing detailed spatial and spectral information,which is fundamental for understanding complex phenomena across various domains.Traditional systems face...Multispectral imaging plays a crucial role in simultaneously capturing detailed spatial and spectral information,which is fundamental for understanding complex phenomena across various domains.Traditional systems face significant challenges,such as large volume,static function,and limited wavelength selectivity.Here,we propose an innovative dynamic reflective multispectral imaging system via a thermally responsive cholesteric liquid crystal based planar lens.By employing advanced photoalignment technology,the phase distribution of a lens is imprinted to the liquid crystal director.The reflection band is reversibly tuned from 450 nm to 750 nm by thermally controlling the helical pitch of the cholesteric liquid crystal,allowing selectively capturing images in different colors.This capability increases imaging versatility,showing great potential in precision agriculture for assessing crop health,noninvasive diagnostics in healthcare,and advanced remote sensing for environmental monitoring.展开更多
Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging du...Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.展开更多
Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems,...Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.展开更多
AIM:To present the 1-year results of a prospective cohort study investigating the efficacy,potential mechanism,and safety of orthokeratology(ortho-k)with different back optic zone diameters(BOZD)for myopia control in ...AIM:To present the 1-year results of a prospective cohort study investigating the efficacy,potential mechanism,and safety of orthokeratology(ortho-k)with different back optic zone diameters(BOZD)for myopia control in children.METHODS:This randomized clinical study was performed between Dec.2020 and Dec.2021.Participants were randomly assigned to three groups wearing ortho-k:5 mm BOZD(5-MM group),5.5 mm BOZD(5.5-MM group),and 6 mm BOZD(6-MM group).The 1-year data were recorded,including axial length,relative peripheral refraction(RPR,measured by multispectral refractive topography,MRT),and visual quality.The contrast sensitivity(CS)was evaluated by CSV-1000 instrument with spatial frequencies of 3,6,12,and 18 cycles/degree(c/d);the corneal higher-order aberrations(HOAs)were measured by iTrace aberration analyzer.The one-way ANOVA was performed to assess the differences between the three groups.The correlation between the change in AL and RPR was calculated by Pearson’s correlation coefficient.RESULTS:The 1-year results of 20,21,and 21 subjects in the 5-MM,5.5-MM,and 6-MM groups,respectively,were presented.There were no statistical differences in baseline age,sex,or ocular parameters between the three groups(all P>0.05).At the 1-year visit,the 5-MM group had lower axial elongation than the 6-MM group(0.07±0.09 vs 0.18±0.11 mm,P=0.001).The 5-MM group had more myopic total RPR(TRPR,P=0.014),with RPR in the 15°–30°(RPR 15–30,P=0.015),30°–45°(RPR 30–45,P=0.011),temporal(RPR-T,P=0.008),and nasal area(RPR-N,P<0.001)than the 6-MM group.RPR 15–30 in the 5.5-MM group was more myopic than that in the 6-MM group(P=0.002),and RPR-N in the 5-MM group was more myopic than that in the 5.5-MM group(P<0.001).There were positive correlations between the axial elongation and the change in TRPR(r=0.756,P<0.001),RPR 15–30(r=0.364,P=0.004),RPR 30–45(r=0.306,P=0.016),and RPR-N(r=0.253,P=0.047).The CS decreased at 3 c/d(P<0.001),and the corneal HOAs increased in the 5-MM group(P=0.030).CONCLUSION:Ortho-k with 5 mm BOZD can control myopia progression more effectively.The mechanism may be associated with greater myopic shifts in RPR.展开更多
AIM:To compare relative peripheral refraction(RPR)in Chinese school children with different refractive errors using multispectral refraction topography(MRT).METHODS:A total of 713 eyes of primary school children[172 e...AIM:To compare relative peripheral refraction(RPR)in Chinese school children with different refractive errors using multispectral refraction topography(MRT).METHODS:A total of 713 eyes of primary school children[172 emmetropia(E),429 low myopia(LM),80 moderate myopia(MM),and 32 low hypermetropia(LH)]aged 10 to 13y were analyzed.RPRs were measured using MRT without mydriasis.MRT results showed RPR at 0-15°(RPR 0-15),15°-30°(RPR 15-30),and 30°-45°(RPR 30-45)annular in the inferior(RPR-I),superior(RPR-S),nasal(RPR-N),and temporal(RPR-T)quadrants.Spherical equivalent(SE)was detected and calculated using an autorefractor.RESULTS:There were significant differences of RPR 15-30 between groups MM[0.02(-0.12;0.18)]and LH[-0.13(-0.36;0.12)](P<0.05),MM and E[-0.06(-0.20;0.10)](P<0.05),and LM[-0.02(-0.15;0.15)]and E(P<0.05).There were also significant differences of RPR 30-45 between groups MM[0.45(0.18;0.74)]and E[0.29(-0.09;0.67)](P<0.05),and LM[0.44(0.14;0.76)]and E(P<0.001).RPR values increased from the hyperopic to medium myopic group in each annular.There were significant differences of RPR-S between groups MM[-0.02(-0.60;0.30)]and E[-0.44(-0.89;-0.04)](P<0.001),and LM[-0.28(-0.71;0.12)]and E(P<0.05).There were also significant differences of RPR-T between groups MM[0.37(0.21;0.78)]and LH[0.14(-0.52;0.50)](P<0.05),LM[0.41(0.06;0.84)]and LH(P<0.05),and LM and E[0.29(-0.10;0.68),P<0.05].A Spearman’s correlation analysis showed a negative correlation between RPR and SE in the 15°-30°(P=0.005),30°-45°(P<0.05)annular(P=0.002),superior(P<0.001),and temporal(P=0.001)quadrants.CONCLUSION:Without pupil dilation,values for RPR 15-30,30-45,RPR-S,and T shows significant differences between myopic eyes and emmetropia,and the differences are negatively correlated with SE.展开更多
Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion s...Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.展开更多
An extreme ultraviolet solar corona multispectral imager can allow direct observation of high temperature coronal plasma,which is related to solar flares,coronal mass ejections and other significant coronal activities...An extreme ultraviolet solar corona multispectral imager can allow direct observation of high temperature coronal plasma,which is related to solar flares,coronal mass ejections and other significant coronal activities.This manuscript proposes a novel end-to-end computational design method for an extreme ultraviolet(EUV)solar corona multispectral imager operating at wavelengths near 100 nm,including a stray light suppression design and computational image recovery.To suppress the strong stray light from the solar disk,an outer opto-mechanical structure is designed to protect the imaging component of the system.Considering the low reflectivity(less than 70%)and strong-scattering(roughness)of existing extreme ultraviolet optical elements,the imaging component comprises only a primary mirror and a curved grating.A Lyot aperture is used to further suppress any residual stray light.Finally,a deep learning computational imaging method is used to correct the individual multi-wavelength images from the original recorded multi-slit data.In results and data,this can achieve a far-field angular resolution below 7",and spectral resolution below 0.05 nm.The field of view is±3 R_(☉)along the multi-slit moving direction,where R☉represents the radius of the solar disk.The ratio of the corona's stray light intensity to the solar center's irradiation intensity is less than 10-6 at the circle of 1.3 R_(☉).展开更多
Precise classification of Light Detection and Ranging(LiDAR)point cloud is a fundamental process in various applications,such as land cover mapping,forestry management,and autonomous driving.Due to the lack of spectra...Precise classification of Light Detection and Ranging(LiDAR)point cloud is a fundamental process in various applications,such as land cover mapping,forestry management,and autonomous driving.Due to the lack of spectral information,the existing research on single wavelength LiDAR classification is limited.Spectral information from images could address this limitation,but data fusion suffers from varying illumination conditions and the registration problem.A novel multispectral LiDAR successfully obtains spatial and spectral information as a brand-new data type,namely,multispectral point cloud,thereby improving classification performance.However,spatial and spectral information of multispectral LiDAR has been processed separately in previous studies,thereby possibly limiting the classification performance of multispectral LiDAR.To explore the potential of this new data type,the current spatial-spectral classification framework for multispectral LiDAR that includes four steps:(1)neighborhood selection,(2)feature extraction and selection,(3)classification,and(4)label smoothing.Three novel highlights were proposed in this spatial-spectral classification framework.(1)We improved the popular eigen entropy-based neighborhood selection by spectral angle match to extract a more precise neighborhood.(2)We evaluated the importance of geometric and spectral features to compare their contributions and selected the most important features to reduce feature redundancy.(3)We conducted spatial label smoothing by a conditional random field,accounting for the spatial and spectral information of the neighborhood points.The proposed method demonstrated by a multispectral LiDAR with three channels:466 nm(blue),527 nm(green),and 628 nm(red).Experimental results demonstrate the effectiveness of the proposed spatial-spectral classification framework.Moreover,this research takes advantages of the complementation of spatial and spectral information,which could benefit more precise neighborhood selection,more effective features,and satisfactory refinement of classification result.Finally,this study could serve as an inspiration for future efficient spatial-spectral process for multispectral point cloud.展开更多
Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classif...Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classification process can be described as information flow from images to maps through a trained classifier.Characterizing the information flow is essential for understanding the classification mechanism,providing solutions that address such theoretical issues as“what is the maximum number of classes that can be classified from a given MRSI?”and“how much information gain can be obtained?”Consequently,two interesting questions naturally arise,i.e.(i)How can we characterize the information flow?and(ii)What is the mathematical form of the information flow?To answer these two questions,this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM.This hypothesis is then supported by kinetic-theory-based experiments.Thereafter,upon such an entropy,a generalized Jarzynski equation is formulated to mathematically model the information flow,which contains such parameters as thermodynamic entropy of MRSI,thermodynamic entropy of LULCM,weighted F1-score(classification accuracy),and total number of classes.This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers.This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification,opening a new door for constructing domain knowledge.展开更多
Two field experiments were conducted in Jiashan and Yuhang towns of Zhejiang Province, China, to study the feasibility of predicting N status of rice using canopy spectral reflectance. The canopy spectral reflectance ...Two field experiments were conducted in Jiashan and Yuhang towns of Zhejiang Province, China, to study the feasibility of predicting N status of rice using canopy spectral reflectance. The canopy spectral reflectance of rice grown with different levels of N inputs was determined at several important growth stages. Statistical analyses showed that as a result of the different levels of N supply, there were significant differences in the N concentrations of canopy leaves at different growth stages. Since spectral reflectance measurements showed that the N status of rice was related to reflectance in the visible and NIR (near-infrared) ranges, observations for rice in 1 nm bandwidths were then converted to bandwidths in the visible and NIR spectral regions with IKONOS (space imaging) bandwidths and vegetation indices being used to predict the N status of rice. The results indicated that canopy reflectance measurements converted to ratio vegetation index (RVI) and normalized difference vegetation index (NDVI) for simulated IKONOS bands provided a better prediction of rice N status than the reflectance measurements in the simulated IKONOS bands themselves. The precision of the developed regression models using RVI and NDVI proved to be very high with R2 ranging from 0.82 to 0.94, and when validated with experimental data from a different site, the results were satisfactory with R2 ranging from 0.55 to 0.70. Thus, the results showed that theoretically it should be possible to monitor N status using remotely sensed data.展开更多
Mapping forests is an important process in managing natural resources.At present,due to spectral resolution limitations,multispectral images do not give a complete separation between different forest species.In contra...Mapping forests is an important process in managing natural resources.At present,due to spectral resolution limitations,multispectral images do not give a complete separation between different forest species.In contrast,advances in remote sensing technologies have provided hyperspectral tools and images as a solution for the determination of species.In this study,spectral signatures for stone pine（Pinus pinea L.） forests were collected using an advanced spectroradiometer ＂ASD FieldSpec 4 Hi-Res＂ with an accuracy of 1 nm.These spectral signatures are used to compare between different multispectral and hyperspectral satellite images.The comparison is based on processing satellite images： hyperspectral Hyperion,hyperspectral CHRIS-Proba,Advanced Land Imager（ALI）,and Landsat 8.Enhancement and classification methods for hyperspectral and multispectral images are investigated and analyzed.In addition,a well-known hyperspectral image classification algorithm,spectral angle mapper（SAM）,has been improved to perform the classification process efficiently based on collected spectral signatures.The results show that the modified SAM is 9% more accurate than the conventional SAM.In addition,experiments indicate that the CHRIS-Proba image is more accurate than Landsat 8（overall accuracy 82%,precision 93%,and Kappa coefficient 0.43 compared to 60,67%,and 0.035,respectively）.Similarly,Hyperion is better than ALI in mapping stone pine（overall accuracy 92%,precision 97%,and Kappa coefficient 0.74 compared to 52,56%,and -0.032,respectively）.展开更多
BACKGROUND Myopia,as one of the common ocular diseases,often occurs in adolescence.In addition to the harm from itself,it may also lead to serious complications.Thus,prevention and control of myopia are attracting mor...BACKGROUND Myopia,as one of the common ocular diseases,often occurs in adolescence.In addition to the harm from itself,it may also lead to serious complications.Thus,prevention and control of myopia are attracting more and more attention.Previous research revealed that single-focal glasses and orthokeratology lenses(OK lenses)played an important part in slowing down myopia and preventing high myopia.AIM To compare the clinical effects of OK lenses and frame glasses against the increase of diopter in adolescent myopia and further explore the mechanism of the OK lens.METHODS Changes in diopter and axial length were collected among 70 adolescent myopia patients(124 eyes)wearing OK lenses for 1 year(group A)and 59 adolescent myopia patients(113 eyes)wearing frame glasses(group B).Refractive states of their retina were inspected through multispectral refraction topography.The obtained hyperopic defocus was analyzed for the mechanism of OK lenses on slowing down the increase of myopic diopter by delaying the increase of ocular axis length and reducing the near hyperopia defocus.RESULTS Teenagers in groups A and B were divided into low myopia(0 D--3.00 D)and moderate myopia(-3.25 D--6.00 D),without statistical differences among gender and age.After 1-year treatment,the increase of diopter and axis length and changes of retinal hyperopic defocus amount of group A were significantly less than those of group B.According to the multiple linear analysis,the retinal defocus in the upper,lower,nasal,and temporal directions had almost the same effect on the total defocus.The amount of peripheral retinal defocus(15°-53°)in group A was significantly lower than that in group B.CONCLUSION Multispectral refraction topography is progressive and instructive in clinical prevention and control of myopia.展开更多
Physical dormancy(PY) commonly present in the seeds of higher plants is believed to be responsible for the germination failure by impermeable seed coat in hard seeds of legume species, instead of physiological dormanc...Physical dormancy(PY) commonly present in the seeds of higher plants is believed to be responsible for the germination failure by impermeable seed coat in hard seeds of legume species, instead of physiological dormancy(PD). In this study, a non-destructive approach involving multispectral imaging was used to successfully identify hard seeds from non-hard seeds in Medicago sativa, with accuracy as high as96.8%–99.0%. We further adopted multiple-omics strategies to investigate the differences of physiology,metabolomics, methylomics, and transcriptomics in alfalfa hard seeds, with non-hard seeds as control.The hard seeds showed dramatically increased antioxidants and 125 metabolites of significant differences in non-targeted metabolomics analysis, which are enriched in the biosynthesis pathways of flavonoids, lipids and hormones, especially with significantly higher ABA, a hormone known to induce dormancy. In our transcriptomics results, the enrichment pathway of “response to abscisic acid” of differential expressed genes(DEG) supported the key role of ABA in metabolomics results. The methylome analysis identified 54,899, 46,216 and 54,452 differential methylation regions for contexts of CpG, CHG and CHH, and 344 DEGs might be regulated by hypermethylation and hypomethylation of promoter and exon regions, including four ABA-and JA-responsive genes. Among 8% hard seeds in seed lots,24.5% still did not germinate after scarifying seed coat, and were named as non-PY hard seeds.Compared to hard seeds, significantly higher contents of ABA/IAA and ABA/JA were identified in nonPY hard seeds, which indicated the potential presence of PD. In summary, the significantly changed metabolites, gene expressions, and methylations all suggested involvement of ABA responses in hard seeds, and germination failure of alfalfa hard seeds was caused by combinational dormancy(PY + PD),rather than PY alone.展开更多
The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field samplin...The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.展开更多
Multispectral and polarization cameras that can simultaneously acquire the spatial,spectral,and polarization characteristics of an object have considerable potential applications in target detection,biomedical imaging...Multispectral and polarization cameras that can simultaneously acquire the spatial,spectral,and polarization characteristics of an object have considerable potential applications in target detection,biomedical imaging,and remote sensing.In this work,we develop a common-aperture optical system that can capture multispectral and polarization information.An off-axis three-mirror optical system is mounted on the front end of the proposed system and used as a common-aperture telescope in the visible light(400 nm-750 nm)and long-wave infrared(LWIR,8μm-12μm)waveband.The system can maintain a wide field of view(4.5°)and it can demonstrate an enhanced identification ability.The off-axis three-mirror system gets rid of central obscuration while further yielding stable system resolution and energy.Light that has passed through the front-end common-aperture reflection system is divided into the visible light and LWIR waveband by a beamsplitter.The two wavebands then converge on two detectors through two groups of lenses.Our simulation results indicate that the proposed system can obtain clear images in each waveband to meet the diverse imaging requirements.展开更多
Four data fusion methods, principle component transform (PCT), brovey transform (BT), smoothing filter-based intensity modulation(SFIM), and hue, saturation, intensity (HSI), are used to merge Landsat-7 ETM+ multispec...Four data fusion methods, principle component transform (PCT), brovey transform (BT), smoothing filter-based intensity modulation(SFIM), and hue, saturation, intensity (HSI), are used to merge Landsat-7 ETM+ multispectral bands with ETM+ panchromatic band. Each of them improves the spatial resolution effectively but distorts the original spectral signatures to some extent. SFIM model can produce optimal fusion data with respect to preservation of spectral integrity. However, it results the most blurred and noisy image if the coregistration between the multispectral and pan images is not accurate enough. The spectral integrity for all methods is preserved better if the original multispectral images are within the spectral range of ETM+ pan image.展开更多
Prorocentrum donghaiense is one of the most common red tide causative dinoflagellates in the Changjiang （Yangtze） River Estuary and the adjacent area of the East China Sea. It causes large-scale blooms in late sprin...Prorocentrum donghaiense is one of the most common red tide causative dinoflagellates in the Changjiang （Yangtze） River Estuary and the adjacent area of the East China Sea. It causes large-scale blooms in late spring and early summer that lead to widespread ecologic and economic damage. A means for distinguish- ing dinoflagellate blooms from diatom （Skeletonema costatum） blooms is desired. On the basis of measure- ments of remote sensing refectance [Rrs（λ）] and inherent optical parameters, the potential of using a mul- tispectral approach is assessed for discriminating the algal blooms due to P. donghaiense from those due to S. costatum. The behavior of two reflectance ratios [R1 = Rrs（560）/Rrs（532） and Re = Rrs（708）/Rrs（665）], suggests that differentiation of P. donghaiense blooms from diatom bloom types is possible from the current band setup of ocean color sensors. It is found that there are two reflectance ratio regimes that indicate a bloom is dominated by P. donghaiense; （1） R1 〉 1.55 and R2 〈 1.0 or （2） R1 〉 1.75 and R2 ≥ 1.0. Various sensitivity analyses are conducted to investigate the effects of the variation in varying levels of chlorophyll concentration and colored dissolved organic matter （CDOM） as well as changes in the backscattering ratio （bbp/bp） on the efficacy of this muitispectral approach. Results indicate that the intensity and inherent op- tical properties of the algal species explain much of the behavior of the two ratios. Although backscattering influences the amplitude of Rrs（λ）, especially in the 530 and 560 nm bands, the discrimination between P. donghaiense and diatoms is not significantly affected by the variation of bbp/bp. Since aCDOM （440） in coastal areas of the ECS is typically lower than 1.0 m-1 in most situations, the presence of CDOM does not interfere with this discrimination, even as SCDOM varies from 0.01 to 0.026 nm-1. Despite all of these effects, the dis- crimination of P. donghaiense blooms from diatom blooms based on multispectral measurements of Rrs（λ） is feasible.展开更多
文摘Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.
基金This research was supported in part by a postdoctoral research fellow appointment to the Agricultural Research Service(ARS)Research Participation Program administered by the Oak Ridge Institute for Science and Education(ORISE)through an interagency agreement between the U.S.Department of Energy(DOE)and the U.S.Department of Agriculture(USDA).
文摘Crop leaf area index(LAI)and biomass are two major biophysical parameters to measure crop growth and health condition.Measuring LAI and biomass in field experiments is a destructive method.Therefore,we focused on the application of unmanned aerial vehicles(UAVs)in agriculture,which is a cost and labor-efficientmethod.Hence,UAV-captured multispectral images were applied to monitor crop growth,identify plant bio-physical conditions,and so on.In this study,we monitored soybean crops using UAV and field experiments.This experiment was conducted at theMAFES(Mississippi Agricultural and Forestry Experiment Station)Pontotoc Ridge-Flatwoods Branch Experiment Station.It followed a randomized block design with five cover crops:Cereal Rye,Vetch,Wheat,MC:mixed Mustard and Cereal Rye,and native vegetation.Planting was made in the fall,and three fertilizer treatments were applied:Synthetic Fertilizer,Poultry Litter,and none,applied before planting the soybean,in a full factorial combination.We monitored soybean reproductive phases at R3(initial pod development),R5(initial seed development),R6(full seed development),and R7(initial maturity)and used UAV multispectral remote sensing for soybean LAI and biomass estimations.The major goal of this study was to assess LAI and biomass estimations from UAV multispectral images in the reproductive stages when the development of leaves and biomass was stabilized.Wemade about fourteen vegetation indices(VIs)fromUAVmultispectral images at these stages to estimate LAI and biomass.Wemodeled LAI and biomass based on these remotely sensed VIs and ground-truth measurements usingmachine learning methods,including linear regression,Random Forest(RF),and support vector regression(SVR).Thereafter,the models were applied to estimate LAI and biomass.According to the model results,LAI was better estimated at the R6 stage and biomass at the R3 stage.Compared to the other models,the RF models showed better estimation,i.e.,an R^(2) of about 0.58–0.68 with an RMSE(rootmean square error)of 0.52–0.60(m^(2)/m^(2))for the LAI and about 0.44–0.64 for R^(2) and 21–26(g dry weight/5 plants)for RMSE of biomass estimation.We performed a leave-one-out cross-validation.Based on cross-validatedmodels with field experiments,we also found that the R6 stage was the best for estimating LAI,and the R3 stage for estimating crop biomass.The cross-validated RF model showed the estimation ability with an R^(2) about 0.25–0.44 and RMSE of 0.65–0.85(m^(2)/m^(2))for LAI estimation;and R^(2) about 0.1–0.31 and an RMSE of about 28–35(g dry weight/5 plants)for crop biomass estimation.This result will be helpful to promote the use of non-destructive remote sensing methods to determine the crop LAI and biomass status,which may bring more efficient crop production and management.
基金funded by the National Key R&D Program of China(2022YFF0706005)National Natural Science Foundation of China(12272407,62275269,62275271,62305387)+3 种基金Foundation of NUDT(ZK23-03)Hunan Provincial Natural Science Foundation of China(2022JJ40552,2023JJ40683)State Key Laboratory of High Performance Computing,NUDT(202201-12)the Hunan Provincial Innovation Foundation for Postgraduate,China(CX20230009).
文摘Most multispectral compatible infrared camouflage devices primarily focus on achieving low emissivity but neglect environmental emissivity matching when environmental emissivity exceeds that of the devices,this creates a"low-emissivity exposure"risk.To address this issue,we develop a tunable multispectral compatible infrared camouflage device using phase change material In3SbTe2(IST).Simulation and experimental results demonstrate that in both the amorphous(aIST)and crystalline(cIST)states,the device achieves simulated plant infrared camouflage and ultra-low emissivity infrared camouflage within the atmospheric window bands(3–5μm and 8–14μm).To address thermal management,it utilizes two non-atmospheric window bands(2.5–3μm and 5–8μm)for heat dissipation.Additionally,laser stealth is realized at three specific wavelengths(1.064μm,1.55μm,and 10.6μm).In the visible spectrum,high absorptivity enables effective visible light camouflage.Adjusting the geometric parameters of top layer structure enables color variation.This work not only highlights potential applications in reversible switching,reconfigurable imaging,and dynamic coding using IST but also offers an effective strategy to counter multispectral detection technology.
基金supported by the National Key Research and Development Program of China(No.2022YFA1203700)the National Natural Science Foundation of China(NSFC)(Nos.62405129 and 62035008)+1 种基金the University Research Project of Guangzhou Education Bureau(No.202235053)the Natural Science Foundation of Jiangsu Province(No.BK20241197).
文摘Multispectral imaging plays a crucial role in simultaneously capturing detailed spatial and spectral information,which is fundamental for understanding complex phenomena across various domains.Traditional systems face significant challenges,such as large volume,static function,and limited wavelength selectivity.Here,we propose an innovative dynamic reflective multispectral imaging system via a thermally responsive cholesteric liquid crystal based planar lens.By employing advanced photoalignment technology,the phase distribution of a lens is imprinted to the liquid crystal director.The reflection band is reversibly tuned from 450 nm to 750 nm by thermally controlling the helical pitch of the cholesteric liquid crystal,allowing selectively capturing images in different colors.This capability increases imaging versatility,showing great potential in precision agriculture for assessing crop health,noninvasive diagnostics in healthcare,and advanced remote sensing for environmental monitoring.
基金National Natural Science Foundation of China(Grant Nos.62005049 and 62072110)Natural Science Foundation of Fujian Province(Grant No.2020J01451).
文摘Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.
基金support by the National Natural Science Foundation of China (Grant No. 62005049)Natural Science Foundation of Fujian Province (Grant Nos. 2020J01451, 2022J05113)Education and Scientific Research Program for Young and Middleaged Teachers in Fujian Province (Grant No. JAT210035)。
文摘Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.
基金Supported by Education Department Foundation of Sichuan Province(No.15ZA0262).
文摘AIM:To present the 1-year results of a prospective cohort study investigating the efficacy,potential mechanism,and safety of orthokeratology(ortho-k)with different back optic zone diameters(BOZD)for myopia control in children.METHODS:This randomized clinical study was performed between Dec.2020 and Dec.2021.Participants were randomly assigned to three groups wearing ortho-k:5 mm BOZD(5-MM group),5.5 mm BOZD(5.5-MM group),and 6 mm BOZD(6-MM group).The 1-year data were recorded,including axial length,relative peripheral refraction(RPR,measured by multispectral refractive topography,MRT),and visual quality.The contrast sensitivity(CS)was evaluated by CSV-1000 instrument with spatial frequencies of 3,6,12,and 18 cycles/degree(c/d);the corneal higher-order aberrations(HOAs)were measured by iTrace aberration analyzer.The one-way ANOVA was performed to assess the differences between the three groups.The correlation between the change in AL and RPR was calculated by Pearson’s correlation coefficient.RESULTS:The 1-year results of 20,21,and 21 subjects in the 5-MM,5.5-MM,and 6-MM groups,respectively,were presented.There were no statistical differences in baseline age,sex,or ocular parameters between the three groups(all P>0.05).At the 1-year visit,the 5-MM group had lower axial elongation than the 6-MM group(0.07±0.09 vs 0.18±0.11 mm,P=0.001).The 5-MM group had more myopic total RPR(TRPR,P=0.014),with RPR in the 15°–30°(RPR 15–30,P=0.015),30°–45°(RPR 30–45,P=0.011),temporal(RPR-T,P=0.008),and nasal area(RPR-N,P<0.001)than the 6-MM group.RPR 15–30 in the 5.5-MM group was more myopic than that in the 6-MM group(P=0.002),and RPR-N in the 5-MM group was more myopic than that in the 5.5-MM group(P<0.001).There were positive correlations between the axial elongation and the change in TRPR(r=0.756,P<0.001),RPR 15–30(r=0.364,P=0.004),RPR 30–45(r=0.306,P=0.016),and RPR-N(r=0.253,P=0.047).The CS decreased at 3 c/d(P<0.001),and the corneal HOAs increased in the 5-MM group(P=0.030).CONCLUSION:Ortho-k with 5 mm BOZD can control myopia progression more effectively.The mechanism may be associated with greater myopic shifts in RPR.
基金Supported by the Shenzhen Science and Technology Program (No.JCYJ20210324142800001).
文摘AIM:To compare relative peripheral refraction(RPR)in Chinese school children with different refractive errors using multispectral refraction topography(MRT).METHODS:A total of 713 eyes of primary school children[172 emmetropia(E),429 low myopia(LM),80 moderate myopia(MM),and 32 low hypermetropia(LH)]aged 10 to 13y were analyzed.RPRs were measured using MRT without mydriasis.MRT results showed RPR at 0-15°(RPR 0-15),15°-30°(RPR 15-30),and 30°-45°(RPR 30-45)annular in the inferior(RPR-I),superior(RPR-S),nasal(RPR-N),and temporal(RPR-T)quadrants.Spherical equivalent(SE)was detected and calculated using an autorefractor.RESULTS:There were significant differences of RPR 15-30 between groups MM[0.02(-0.12;0.18)]and LH[-0.13(-0.36;0.12)](P<0.05),MM and E[-0.06(-0.20;0.10)](P<0.05),and LM[-0.02(-0.15;0.15)]and E(P<0.05).There were also significant differences of RPR 30-45 between groups MM[0.45(0.18;0.74)]and E[0.29(-0.09;0.67)](P<0.05),and LM[0.44(0.14;0.76)]and E(P<0.001).RPR values increased from the hyperopic to medium myopic group in each annular.There were significant differences of RPR-S between groups MM[-0.02(-0.60;0.30)]and E[-0.44(-0.89;-0.04)](P<0.001),and LM[-0.28(-0.71;0.12)]and E(P<0.05).There were also significant differences of RPR-T between groups MM[0.37(0.21;0.78)]and LH[0.14(-0.52;0.50)](P<0.05),LM[0.41(0.06;0.84)]and LH(P<0.05),and LM and E[0.29(-0.10;0.68),P<0.05].A Spearman’s correlation analysis showed a negative correlation between RPR and SE in the 15°-30°(P=0.005),30°-45°(P<0.05)annular(P=0.002),superior(P<0.001),and temporal(P=0.001)quadrants.CONCLUSION:Without pupil dilation,values for RPR 15-30,30-45,RPR-S,and T shows significant differences between myopic eyes and emmetropia,and the differences are negatively correlated with SE.
基金supported by the Henan Provincial Science and Technology Research Project under Grants 232102211006,232102210044,232102211017,232102210055 and 222102210214the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205+1 种基金the Undergraduate Universities Smart Teaching Special Research Project of Henan Province under Grant Jiao Gao[2021]No.489-29the Doctor Natural Science Foundation of Zhengzhou University of Light Industry under Grants 2021BSJJ025 and 2022BSJJZK13.
文摘Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.
基金This study is partially supported by the National Natural Science Foundation of China(NSFC)(62005120,62125504).
文摘An extreme ultraviolet solar corona multispectral imager can allow direct observation of high temperature coronal plasma,which is related to solar flares,coronal mass ejections and other significant coronal activities.This manuscript proposes a novel end-to-end computational design method for an extreme ultraviolet(EUV)solar corona multispectral imager operating at wavelengths near 100 nm,including a stray light suppression design and computational image recovery.To suppress the strong stray light from the solar disk,an outer opto-mechanical structure is designed to protect the imaging component of the system.Considering the low reflectivity(less than 70%)and strong-scattering(roughness)of existing extreme ultraviolet optical elements,the imaging component comprises only a primary mirror and a curved grating.A Lyot aperture is used to further suppress any residual stray light.Finally,a deep learning computational imaging method is used to correct the individual multi-wavelength images from the original recorded multi-slit data.In results and data,this can achieve a far-field angular resolution below 7",and spectral resolution below 0.05 nm.The field of view is±3 R_(☉)along the multi-slit moving direction,where R☉represents the radius of the solar disk.The ratio of the corona's stray light intensity to the solar center's irradiation intensity is less than 10-6 at the circle of 1.3 R_(☉).
基金supported by the National Natural Science Foundation of China[grant number 41971307]Fundamental Research Funds for the Central Universities[grant number 2042022kf1200,2042023kf0217]+1 种基金Wuhan University Specific Fund for Major School-level Internationalization InitiativesLIESMARS Special Research Funding.
文摘Precise classification of Light Detection and Ranging(LiDAR)point cloud is a fundamental process in various applications,such as land cover mapping,forestry management,and autonomous driving.Due to the lack of spectral information,the existing research on single wavelength LiDAR classification is limited.Spectral information from images could address this limitation,but data fusion suffers from varying illumination conditions and the registration problem.A novel multispectral LiDAR successfully obtains spatial and spectral information as a brand-new data type,namely,multispectral point cloud,thereby improving classification performance.However,spatial and spectral information of multispectral LiDAR has been processed separately in previous studies,thereby possibly limiting the classification performance of multispectral LiDAR.To explore the potential of this new data type,the current spatial-spectral classification framework for multispectral LiDAR that includes four steps:(1)neighborhood selection,(2)feature extraction and selection,(3)classification,and(4)label smoothing.Three novel highlights were proposed in this spatial-spectral classification framework.(1)We improved the popular eigen entropy-based neighborhood selection by spectral angle match to extract a more precise neighborhood.(2)We evaluated the importance of geometric and spectral features to compare their contributions and selected the most important features to reduce feature redundancy.(3)We conducted spatial label smoothing by a conditional random field,accounting for the spatial and spectral information of the neighborhood points.The proposed method demonstrated by a multispectral LiDAR with three channels:466 nm(blue),527 nm(green),and 628 nm(red).Experimental results demonstrate the effectiveness of the proposed spatial-spectral classification framework.Moreover,this research takes advantages of the complementation of spatial and spectral information,which could benefit more precise neighborhood selection,more effective features,and satisfactory refinement of classification result.Finally,this study could serve as an inspiration for future efficient spatial-spectral process for multispectral point cloud.
基金supported by the National Natural Science Foundation of China[grant number 41930104]by the Research Grants Council of Hong Kong[grant number PolyU 152219/18E].
文摘Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classification process can be described as information flow from images to maps through a trained classifier.Characterizing the information flow is essential for understanding the classification mechanism,providing solutions that address such theoretical issues as“what is the maximum number of classes that can be classified from a given MRSI?”and“how much information gain can be obtained?”Consequently,two interesting questions naturally arise,i.e.(i)How can we characterize the information flow?and(ii)What is the mathematical form of the information flow?To answer these two questions,this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM.This hypothesis is then supported by kinetic-theory-based experiments.Thereafter,upon such an entropy,a generalized Jarzynski equation is formulated to mathematically model the information flow,which contains such parameters as thermodynamic entropy of MRSI,thermodynamic entropy of LULCM,weighted F1-score(classification accuracy),and total number of classes.This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers.This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification,opening a new door for constructing domain knowledge.
基金Project supported by the National Natural Science Foundation of China (Nos. 30070444 and 40201021)the British Council (No. SHA/992/308)the Doctor Foundation of Qingdao University of Science and Technology.
文摘Two field experiments were conducted in Jiashan and Yuhang towns of Zhejiang Province, China, to study the feasibility of predicting N status of rice using canopy spectral reflectance. The canopy spectral reflectance of rice grown with different levels of N inputs was determined at several important growth stages. Statistical analyses showed that as a result of the different levels of N supply, there were significant differences in the N concentrations of canopy leaves at different growth stages. Since spectral reflectance measurements showed that the N status of rice was related to reflectance in the visible and NIR (near-infrared) ranges, observations for rice in 1 nm bandwidths were then converted to bandwidths in the visible and NIR spectral regions with IKONOS (space imaging) bandwidths and vegetation indices being used to predict the N status of rice. The results indicated that canopy reflectance measurements converted to ratio vegetation index (RVI) and normalized difference vegetation index (NDVI) for simulated IKONOS bands provided a better prediction of rice N status than the reflectance measurements in the simulated IKONOS bands themselves. The precision of the developed regression models using RVI and NDVI proved to be very high with R2 ranging from 0.82 to 0.94, and when validated with experimental data from a different site, the results were satisfactory with R2 ranging from 0.55 to 0.70. Thus, the results showed that theoretically it should be possible to monitor N status using remotely sensed data.
基金funded by the Lebanese National Council for Scientific Research(Mapping Stone Pine Forests in Lebanon)
文摘Mapping forests is an important process in managing natural resources.At present,due to spectral resolution limitations,multispectral images do not give a complete separation between different forest species.In contrast,advances in remote sensing technologies have provided hyperspectral tools and images as a solution for the determination of species.In this study,spectral signatures for stone pine（Pinus pinea L.） forests were collected using an advanced spectroradiometer ＂ASD FieldSpec 4 Hi-Res＂ with an accuracy of 1 nm.These spectral signatures are used to compare between different multispectral and hyperspectral satellite images.The comparison is based on processing satellite images： hyperspectral Hyperion,hyperspectral CHRIS-Proba,Advanced Land Imager（ALI）,and Landsat 8.Enhancement and classification methods for hyperspectral and multispectral images are investigated and analyzed.In addition,a well-known hyperspectral image classification algorithm,spectral angle mapper（SAM）,has been improved to perform the classification process efficiently based on collected spectral signatures.The results show that the modified SAM is 9% more accurate than the conventional SAM.In addition,experiments indicate that the CHRIS-Proba image is more accurate than Landsat 8（overall accuracy 82%,precision 93%,and Kappa coefficient 0.43 compared to 60,67%,and 0.035,respectively）.Similarly,Hyperion is better than ALI in mapping stone pine（overall accuracy 92%,precision 97%,and Kappa coefficient 0.74 compared to 52,56%,and -0.032,respectively）.
文摘BACKGROUND Myopia,as one of the common ocular diseases,often occurs in adolescence.In addition to the harm from itself,it may also lead to serious complications.Thus,prevention and control of myopia are attracting more and more attention.Previous research revealed that single-focal glasses and orthokeratology lenses(OK lenses)played an important part in slowing down myopia and preventing high myopia.AIM To compare the clinical effects of OK lenses and frame glasses against the increase of diopter in adolescent myopia and further explore the mechanism of the OK lens.METHODS Changes in diopter and axial length were collected among 70 adolescent myopia patients(124 eyes)wearing OK lenses for 1 year(group A)and 59 adolescent myopia patients(113 eyes)wearing frame glasses(group B).Refractive states of their retina were inspected through multispectral refraction topography.The obtained hyperopic defocus was analyzed for the mechanism of OK lenses on slowing down the increase of myopic diopter by delaying the increase of ocular axis length and reducing the near hyperopia defocus.RESULTS Teenagers in groups A and B were divided into low myopia(0 D--3.00 D)and moderate myopia(-3.25 D--6.00 D),without statistical differences among gender and age.After 1-year treatment,the increase of diopter and axis length and changes of retinal hyperopic defocus amount of group A were significantly less than those of group B.According to the multiple linear analysis,the retinal defocus in the upper,lower,nasal,and temporal directions had almost the same effect on the total defocus.The amount of peripheral retinal defocus(15°-53°)in group A was significantly lower than that in group B.CONCLUSION Multispectral refraction topography is progressive and instructive in clinical prevention and control of myopia.
基金supported by the earmarked fund for CARS (CARS-34)National Key Research and Development Program of China (2022YFD1300804)the Key R&D Project of Sichuan Science and Technology Program(2023YFSY0012)。
文摘Physical dormancy(PY) commonly present in the seeds of higher plants is believed to be responsible for the germination failure by impermeable seed coat in hard seeds of legume species, instead of physiological dormancy(PD). In this study, a non-destructive approach involving multispectral imaging was used to successfully identify hard seeds from non-hard seeds in Medicago sativa, with accuracy as high as96.8%–99.0%. We further adopted multiple-omics strategies to investigate the differences of physiology,metabolomics, methylomics, and transcriptomics in alfalfa hard seeds, with non-hard seeds as control.The hard seeds showed dramatically increased antioxidants and 125 metabolites of significant differences in non-targeted metabolomics analysis, which are enriched in the biosynthesis pathways of flavonoids, lipids and hormones, especially with significantly higher ABA, a hormone known to induce dormancy. In our transcriptomics results, the enrichment pathway of “response to abscisic acid” of differential expressed genes(DEG) supported the key role of ABA in metabolomics results. The methylome analysis identified 54,899, 46,216 and 54,452 differential methylation regions for contexts of CpG, CHG and CHH, and 344 DEGs might be regulated by hypermethylation and hypomethylation of promoter and exon regions, including four ABA-and JA-responsive genes. Among 8% hard seeds in seed lots,24.5% still did not germinate after scarifying seed coat, and were named as non-PY hard seeds.Compared to hard seeds, significantly higher contents of ABA/IAA and ABA/JA were identified in nonPY hard seeds, which indicated the potential presence of PD. In summary, the significantly changed metabolites, gene expressions, and methylations all suggested involvement of ABA responses in hard seeds, and germination failure of alfalfa hard seeds was caused by combinational dormancy(PY + PD),rather than PY alone.
基金funded by the Key Research and Development Program of Shaanxi Province of China(2022NY-063)the Chinese Universities Scientific Fund(2452020018).
文摘The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.
基金Project supported by the National Natural Science Foundation of china(Grant No.61471039)
文摘Multispectral and polarization cameras that can simultaneously acquire the spatial,spectral,and polarization characteristics of an object have considerable potential applications in target detection,biomedical imaging,and remote sensing.In this work,we develop a common-aperture optical system that can capture multispectral and polarization information.An off-axis three-mirror optical system is mounted on the front end of the proposed system and used as a common-aperture telescope in the visible light(400 nm-750 nm)and long-wave infrared(LWIR,8μm-12μm)waveband.The system can maintain a wide field of view(4.5°)and it can demonstrate an enhanced identification ability.The off-axis three-mirror system gets rid of central obscuration while further yielding stable system resolution and energy.Light that has passed through the front-end common-aperture reflection system is divided into the visible light and LWIR waveband by a beamsplitter.The two wavebands then converge on two detectors through two groups of lenses.Our simulation results indicate that the proposed system can obtain clear images in each waveband to meet the diverse imaging requirements.
文摘Four data fusion methods, principle component transform (PCT), brovey transform (BT), smoothing filter-based intensity modulation(SFIM), and hue, saturation, intensity (HSI), are used to merge Landsat-7 ETM+ multispectral bands with ETM+ panchromatic band. Each of them improves the spatial resolution effectively but distorts the original spectral signatures to some extent. SFIM model can produce optimal fusion data with respect to preservation of spectral integrity. However, it results the most blurred and noisy image if the coregistration between the multispectral and pan images is not accurate enough. The spectral integrity for all methods is preserved better if the original multispectral images are within the spectral range of ETM+ pan image.
基金The National Basic Research Program of China (973 Program) under contract No.2013CB430302the National High Technology Research and Development Program of China (863 Program) under contract No.2007AA092002+2 种基金the National Natural Science Foundation of China under contract No.41206170the public science and technology research funds projects of the ocean under contract No.201005030scientific research fund of the Second Institute of Oceanography,SOA under contract No.JG1212
文摘Prorocentrum donghaiense is one of the most common red tide causative dinoflagellates in the Changjiang （Yangtze） River Estuary and the adjacent area of the East China Sea. It causes large-scale blooms in late spring and early summer that lead to widespread ecologic and economic damage. A means for distinguish- ing dinoflagellate blooms from diatom （Skeletonema costatum） blooms is desired. On the basis of measure- ments of remote sensing refectance [Rrs（λ）] and inherent optical parameters, the potential of using a mul- tispectral approach is assessed for discriminating the algal blooms due to P. donghaiense from those due to S. costatum. The behavior of two reflectance ratios [R1 = Rrs（560）/Rrs（532） and Re = Rrs（708）/Rrs（665）], suggests that differentiation of P. donghaiense blooms from diatom bloom types is possible from the current band setup of ocean color sensors. It is found that there are two reflectance ratio regimes that indicate a bloom is dominated by P. donghaiense; （1） R1 〉 1.55 and R2 〈 1.0 or （2） R1 〉 1.75 and R2 ≥ 1.0. Various sensitivity analyses are conducted to investigate the effects of the variation in varying levels of chlorophyll concentration and colored dissolved organic matter （CDOM） as well as changes in the backscattering ratio （bbp/bp） on the efficacy of this muitispectral approach. Results indicate that the intensity and inherent op- tical properties of the algal species explain much of the behavior of the two ratios. Although backscattering influences the amplitude of Rrs（λ）, especially in the 530 and 560 nm bands, the discrimination between P. donghaiense and diatoms is not significantly affected by the variation of bbp/bp. Since aCDOM （440） in coastal areas of the ECS is typically lower than 1.0 m-1 in most situations, the presence of CDOM does not interfere with this discrimination, even as SCDOM varies from 0.01 to 0.026 nm-1. Despite all of these effects, the dis- crimination of P. donghaiense blooms from diatom blooms based on multispectral measurements of Rrs（λ） is feasible.
