Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly diffic...Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly difficult mines and meet the requirements of environmental protection and safety regulations.It promotes the development of a circular economy in mines through the development of lowgrade resources and the resource utilization of waste,and extends the service life of mines.The mass concentration of solid content(abbreviated as“concentration”)is a critical parameter for CPB.However,discrepancies often arise between the on-site measurements and the pre-designed values due to factors such as groundwater inflow and segregation within the goaf,which cannot be evaluated after the solidification of CPB.This paper innovatively provides an in-situ non-destructive approach to identify the real concentration of CPB after curing for certain days using hyperspectral imaging(HSI)technology.Initially,the spectral variation patterns under different concentration conditions were investigated through hyperspectral scanning experiments on CPB samples.The results demonstrate that as the CPB concentration increases from 61wt%to 73wt%,the overall spectral reflectance gradually increases,with two distinct absorption peaks observed at 1407 and 1917 nm.Notably,the reflectance at 1407 nm exhibited a strong linear relationship with the concentration.Subsequently,the K-nearest neighbors(KNN)and support vector machine(SVM)algorithms were employed to classify and identify different concentrations.The study revealed that,with the KNN algorithm,the highest accuracy was achieved when K(number of nearest neighbors)was 1,although this resulted in overfitting.When K=3,the model displayed the optimal balance between accuracy and stability,with an accuracy of 95.03%.In the SVM algorithm,the highest accuracy of 98.24%was attained with parameters C(regularization parameter)=200 and Gamma(kernel coefficient)=10.A comparative analysis of precision,accuracy,and recall further highlighted that the SVM provided superior stability and precision for identifying CPB concentration.Thus,HSI technology offers an effective solution for the in-situ,non-destructive monitoring of CPB concentration,presenting a promising approach for optimizing and controlling CPB characteristic parameters.展开更多
The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textile...The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.展开更多
In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering...In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.展开更多
Imaging hyperspectral technology has distinctive advantages of non-destructive and non-contact measurement,and the integration of spectral and spatial data.These characteristics present new methodologies for intellige...Imaging hyperspectral technology has distinctive advantages of non-destructive and non-contact measurement,and the integration of spectral and spatial data.These characteristics present new methodologies for intelligent geological sensing in tunnels and other underground engineering projects.However,the in situ acquisition and rapid classification of hyperspectral images in underground still faces great challenges,including the difficulty in obtaining uniform hyperspectral images and the complexity of deploying sophisticated models on mobile platforms.This study proposes an intelligent lithology identification method based on partition feature extraction of hyperspectral images.Firstly,pixel-level hyperspectral information from representative lithological regions is extracted and fused to obtain rock hyperspectral image partition features.Subsequently,an SG-SNV-PCA-DNN(SSPD)model specifically designed for optimizing rock hyperspectral data,performing spectral dimensionality reduction,and identifying lithology is integrated.In an experimental study involving 3420 hyperspectral images,the SSPD identification model achieved the highest accuracy in the testing set,reaching 98.77%.Moreover,the speed of the SSPD model was found to be 18.5%faster than that of the unprocessed model,with an accuracy improvement of 5.22%.In contrast,the ResNet-101 model,used for point-by-point identification based on non-partitioned features,achieved a maximum accuracy of 97.86%in the testing set.In addition,the partition feature extraction methods significantly reduce computational complexity.An objective evaluation of various models demonstrated that the SSPD model exhibited superior performance,achieving a precision(P)of 99.46%,a recall(R)of 99.44%,and F1 score(F1)of 99.45%.Additionally,a pioneering in situ detection work was carried out in a tunnel using underground hyperspectral imaging technology.展开更多
In this work,one-step growth models using hyperspectral imaging(HSI)(400-1000 nm)were successfully developed in order to estimate the microbial loads,minimum growth temperature(T_(min))and maximum specific growth rate...In this work,one-step growth models using hyperspectral imaging(HSI)(400-1000 nm)were successfully developed in order to estimate the microbial loads,minimum growth temperature(T_(min))and maximum specific growth rate(μ_(max))of Brochothrix thermosphacta in chilled beef at isothermal temperatures(4-25℃).Three different methods were compared for model development,particularly using(Model Ⅰ)the predicted microbial loads from partial least squares regression of the whole spectral variables;(Model Ⅱ)the selected spectral variables related to microbial loads;and(Model Ⅲ)the first principal scores of HSI spectra by principal component analysis.Consequently,Model Ⅰ showed the best ability to predict the microbial loads of B.thermosphacta,with the coefficient of determination(R_(v)^(2))and root mean square error in internal validation(RMSEV)of 0.921 and 0.498(lg(CFU/g)).The T_(min)(-12.32℃)andμmax can be well estimated with R^(2) and root mean square error(RMSE)of 0.971 and 0.276(lg(CFU/g)),respectively.The upward trend ofμmax with temperature was similar to that of the plate count method.HSI technique thus can be used as a simple method for one-step growth simulation of B.thermosphacta in chilled beef during storage.展开更多
Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic spectrum.However,due to sensor limitations and imperfections during ...Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic spectrum.However,due to sensor limitations and imperfections during the image acquisition and transmission phases,noise is introduced into the acquired image,which can have a negative impact on downstream analyses such as classification,target tracking,and spectral unmixing.Noise in hyperspectral images(HSI)is modelled as a combination from several sources,including Gaussian/impulse noise,stripes,and deadlines.An HSI restoration method for such a mixed noise model is proposed.First,a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data as well as the sparse/impulse noise levels.Second,a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image gradients.Third,to model the sparse nature of impulse noise,anℓ_(1)−norm over the impulse noise gradient is used.Because the proposed methodology employs two distinct priors,the authors refer to it as the hyperspectral dual prior(HySpDualP)denoiser.To the best of authors'knowledge,this joint optimisation framework is the first attempt in this direction.To handle the non-smooth and nonconvex nature of the generalℓ_(p)−norm-based regularisation term,a generalised shrinkage/thresholding(GST)solver is employed.Finally,an efficient split-Bregman approach is used to solve the resulting optimisation problem.Experimental results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’proposed model outperforms state-of-the-art methods,both visually and in terms of various image quality assessment metrics.展开更多
Potassium(K)is a highly mobile nutrient element that continuously adjusts its demand strategy among and within cotton leaves through redistribution,indirectly leading to variations in the leaf potassium content(LKC,%)...Potassium(K)is a highly mobile nutrient element that continuously adjusts its demand strategy among and within cotton leaves through redistribution,indirectly leading to variations in the leaf potassium content(LKC,%)at different leaf positions.However,due to the interaction between light and leaf age,leaf sensitivity to this change varies at different positions,including the reflection and absorption of the spectrum.Selecting the optimal leaf position for monitoring is a crucial factor in the rapid and accurate evaluation of cotton LKC using spectral remote sensing technology.Therefore,this study proposes a comprehensive multi-leaf position estimation model based on the vertical distribution characteristics of LKC from top to bottom,aiming to achieve an accurate estimation of cotton LKC and optimize the strategy for selecting the monitored leaf position.Between 2020 and 2021,we collected hyperspectral imaging data of the main stem leaves at different positions from top to bottom(Li,i=1,2,3,...,n)during the cotton budding,flowering,and boll-setting stages.Vertical distribution characteristics,sensitivity differences,and spectral correlations of LKC at different leaf positions were investigated.Additionally,the optimal range of the dominant leaf position for monitoring was determined.Partial least squares regression(PLSR),random forest regression(RFR),support vector machine regression(SVR),and the entropy weight method(EWM)were employed to develop LKC estimation models for single-and multi-leaf positions.The results showed a vertical heterogeneous distribution of cotton LKC,with LKC initially increasing and then gradually decreasing from top to bottom;the average LKC of cotton reached its maximum value at the flowering stage.The upper leaf position demonstrated greater sensitivity to K and exhibited a stronger correlation with the spectrum.The selected dominant leaf positions for the three growth stages were L1-L5,L1-L4,and L1-L2,respectively.Based on the dominant leaf position monitoring range,the optimal single leaf position models for estimating LKC during the three growth stages were PLSR-L4,PLSR-L1,and SVR-L2,with the coefficient of determination of the validation set(R2val)being 0.786,0.580,and 0.768,and the root-mean-square error of the validation set(RMSEval)being 0.168,0.197,and 0.191,respectively.The multi-leaf position LKC estimation model was constructed by EWM with R2val being 0.887,0.728,and 0.703,and RMSEval being 0.134,0.172,and 0.209,respectively.In contrast,the newly developed multi-leaf position comprehensive estimation model yielded superior results,improving the model’s stability based on high accuracy,especially during the budding and flowering stages.These findings hold significant importance for investigating cotton LKC spectral models and selecting suitable leaf positions for field monitoring.展开更多
Quantitative inversion is a major topic in remote sensing science.The development of visible light-based hyperspectral reconstruction techniques has opened novel prospects for low-cost,high-precision remote sensing in...Quantitative inversion is a major topic in remote sensing science.The development of visible light-based hyperspectral reconstruction techniques has opened novel prospects for low-cost,high-precision remote sensing inversion in agriculture.The aim of this study was to assess the effectiveness of hyperspectral reconstruction technology in agricultural remote sensing applications.Hyperspectral images were reconstructed using the MST++hyperspectral reconstruction model and compared with the original visible light images in terms of their correlations with physiological parameters,the accuracy of single-feature modeling,and the accuracy of combined feature modeling.The results showed that compared to the visible light image,the reconstructed data exhibited a stronger correlation with the measured physiological parameters,and the accuracy was improved for both the single feature and combined feature inversion modes.However,compared to multispectral sensors,hyperspectral reconstruction provided limited improvement of the inversion model accuracy.The results suggest that for physiological parameters that are not easy to observe directly,deep mining of features in visible light data through hyperspectral reconstruction technology can improve the accuracy of the inversion model.However,appropriate feature selection and simple models are more suitable for the remote sensing inversion task of traditional agronomic plot experiments.To strengthen the application of hyperspectral reconstruction technology in agricultural remote sensing,further development is necessary with broader wavelength ranges and more diverse agricultural scenarios.展开更多
BACKGROUND Hyperspectral imaging(HSI)offers useful information on organ quality and has already been successfully used in kidney and liver transplantation to assess transplanted organs.Up to now,there is no case repor...BACKGROUND Hyperspectral imaging(HSI)offers useful information on organ quality and has already been successfully used in kidney and liver transplantation to assess transplanted organs.Up to now,there is no case report in the literature describing HSI for quality assessment of a machine perfused donor liver.The allocated liver from a 49-year-old female donor(161 cm,70 kg)was perfused with the OrganOx®normothermic machine perfusion system in the recommended way.Organ quality assessment was performed based on laboratory values at defined time points.In addition,the final evaluation of the liver comprised macroscopic findings and HSI of each liver segment.After discarding the organ,biopsies were taken from each segment and correlated with the results of the HSI.CASE SUMMARY The donor liver’s size(29 cm×17 cm×11 cm)and weight of 2180 g posed challenges for adequate placement within the organ container.Baseline biopsy of the liver revealed no evidence of fibrosis,steatosis or inflammation.An hour after perfusion start,measurements of the perfusate indicated a pH of 7.18,a glucose level of 404 mg/dL,and a lactate level of 1.7 mmol/L.Throughout perfusion,a significant decline in glucose levels began at the fourth hour,reaching a nadir of 20 mg/dL after eight hours.Concurrently,lactate levels steadily rose,peaking at 4.9 mmol/L after the total perfusion time of 12 hours.Macroscopic alterations(signs of congestion and reduced blood circulation)on the liver’s surface were noted,particularly pronounced in segments 2,3,and 8.HSI of these areas unveiled significant reduced oxygenation.Consequently,based on all these observations,the decision was made to discard the organ.Histological examination of the altered regions revealed congestion,necrotic changes,and dissociation of CONCLUSION This case report describes the integration of HSI in the decision making of the decline of a 49-year-old machine perfused donor liver.HSI offered useful information concerning the tissue morphology and graft viability and could therefore be a useful additional tool in assessing donor liver quality before transplantation.展开更多
Hyperspectral image(HSI)classification is crucial for numerous remote sensing applications.Traditional deep learning methods may miss pixel relationships and context,leading to inefficiencies.This paper introduces the...Hyperspectral image(HSI)classification is crucial for numerous remote sensing applications.Traditional deep learning methods may miss pixel relationships and context,leading to inefficiencies.This paper introduces the spectral band graph convolutional and attention-enhanced CNN joint network(SGCCN),a novel approach that harnesses the power of spectral band graph convolutions for capturing long-range relationships,utilizes local perception of attention-enhanced multi-level convolutions for local spatial feature and employs a dynamic attention mechanism to enhance feature extraction.The SGCCN integrates spectral and spatial features through a self-attention fusion network,significantly improving classification accuracy and efficiency.The proposed method outperforms existing techniques,demonstrating its effectiveness in handling the challenges associated with HSI data.展开更多
Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-ban...Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-band spectra,hyperspectral technology has become a crucial tool to monitor crop diseases using remote sensing.However,existing continuous wavelet analysis(CWA)methods suffer from feature redundancy issues,while the continuous wavelet projection algorithm(CWPA),an optimization approach for feature selection,has not been fully validated to monitor plant diseases.This study utilized rice bacterial leaf blight(BLB)as an example by evaluating the performance of four wavelet basis functions-Gaussian2,Mexican hat,Meyer,andMorlet-within theCWAandCWPAframeworks.Additionally,the classification models were constructed using the k-nearest neighbors(KNN),randomforest(RF),and Naïve Bayes(NB)algorithms.The results showed the following:(1)Compared to traditional CWA,CWPA significantly reduced the number of required features.Under the CWPA framework,almost all the model combinations achieved maximum classification accuracy with only one feature.In contrast,the CWA framework required three to seven features.(2)Thechoice of wavelet basis functions markedly affected the performance of themodel.Of the four functions tested,the Meyer wavelet demonstrated the best overall performance in both the CWPA and CWA frameworks.(3)Under theCWPAframework,theMeyer-KNNandMeyer-NBcombinations achieved the highest overall accuracy of 93.75%using just one feature.In contrast,under the CWA framework,the CWA-RF combination achieved comparable accuracy(93.75%)but required six features.This study verified the technical advantages of CWPA for monitoring crop diseases,identified an optimal wavelet basis function selection scheme,and provided reliable technical support to precisely monitor BLB in rice(Oryza sativa).Moreover,the proposed methodological framework offers a scalable approach for the early diagnosis and assessment of plant stress,which can contribute to improved accuracy and timeliness when plant stress is monitored.展开更多
Due to the limitations of existing imaging hardware, obtaining high-resolution hyperspectral images is challenging. Hyperspectral image super-resolution(HSI SR) has been a very attractive research topic in computer vi...Due to the limitations of existing imaging hardware, obtaining high-resolution hyperspectral images is challenging. Hyperspectral image super-resolution(HSI SR) has been a very attractive research topic in computer vision, attracting the attention of many researchers. However, most HSI SR methods focus on the tradeoff between spatial resolution and spectral information, and cannot guarantee the efficient extraction of image information. In this paper, a multidimensional features network(MFNet) for HSI SR is proposed, which simultaneously learns and fuses the spatial,spectral, and frequency multidimensional features of HSI. Spatial features contain rich local details,spectral features contain the information and correlation between spectral bands, and frequency feature can reflect the global information of the image and can be used to obtain the global context of HSI. The fusion of the three features can better guide image super-resolution, to obtain higher-quality high-resolution hyperspectral images. In MFNet, we use the frequency feature extraction module(FFEM) to extract the frequency feature. On this basis, a multidimensional features extraction module(MFEM) is designed to learn and fuse multidimensional features. In addition, experimental results on two public datasets demonstrate that MFNet achieves state-of-the-art performance.展开更多
Aflatoxin B1(AFB1)is a toxic fungal metabolite that contaminates almonds from cultivation to harvesting.It leads to chronic health problems and significant economic loss to the producers.Therefore,a fast and non-invas...Aflatoxin B1(AFB1)is a toxic fungal metabolite that contaminates almonds from cultivation to harvesting.It leads to chronic health problems and significant economic loss to the producers.Therefore,a fast and non-invasive detection technique is crucial for safeguarding food safety by swiftly identifying and eliminating contaminated almonds from the supply chain.Hyperspectral imaging has been explored as a potential non-destructive technology for detecting AFB1.However,the diverse geometries of almonds present a significant challenge on acquired images,thereby impacting the accuracy of the developed prediction and classification models.This study investigates the effectiveness of short-wave infrared(SwIR)hyperspectral imaging combined with deep learning for detecting AFB1 in almonds of varying geometries.Initially,partial least squares regression(PLSR)and support vector machine(SvM)regression models were evaluated for quantification,while SVM and quadratic discriminant analysis(QDA)classifiers were applied for classification.The results indicated that spectral responses varied with almond thickness,making quantification models unreliable for industrial applications.The Competitive Adaptive Reweighted Sampling(CARS)algorithm was employed to identify key spectral features for developing multi-spectral AFB1 classification models to evaluate the feasibility of high-speed,accurate in-line detection.The deep learning approach significantly outperformed traditional machine learning models,with the pre-trained Inception V3 network achieving a cross-validation accuracy of 84.82%,an F1-score of 0.8522,and an area under curve of 0.893.These findings highlight the superiority of deep learning-based hyperspectral imaging for accurate and reliable AFB1 detection in almonds with diverse shapes and thicknesses.展开更多
In hyperspectral image classification(HSIC),accurately extracting spatial and spectral information from hyperspectral images(HSI)is crucial for achieving precise classification.However,due to low spatial resolution an...In hyperspectral image classification(HSIC),accurately extracting spatial and spectral information from hyperspectral images(HSI)is crucial for achieving precise classification.However,due to low spatial resolution and complex category boundary,mixed pixels containing features from multiple classes are inevitable in HSIs.Additionally,the spectral similarity among different classes challenge for extracting distinctive spectral features essential for HSIC.To address the impact of mixed pixels and spectral similarity for HSIC,we propose a central-pixel guiding sub-pixel and sub-channel convolution network(CP-SPSC)to extract more precise spatial and spectral features.Firstly,we designed spatial attention(CP-SPA)and spectral attention(CP-SPE)informed by the central pixel to effectively reduce spectral interference of irrelevant categories in the same patch.Furthermore,we use CP-SPA to guide 2D sub-pixel convolution(SPConv2d)to capture spatial features finer than the pixel level.Meanwhile,CP-SPE is also utilized to guide 1D sub-channel con-volution(SCConv1d)in selecting more precise spectral channels.For fusing spatial and spectral information at the feature-level,the spectral feature extension transformation module(SFET)adopts mirror-padding and snake permutation to transform 1D spectral information of the center pixel into 2D spectral features.Experiments on three popular datasets demonstrate that ours out-performs several state-of-the-art methods in accuracy.展开更多
Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferome...Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep learning.Firstly, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based algorithms.In addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.展开更多
In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land mapping.This study pr...In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land mapping.This study presents an innovative framework for assessing HSI band quality and reconstructing the low-quality bands,based on the Prophet model.By introducing a comprehensive quality metric to start,the authors approach factors in both spatial and spectral characteristics across local and global scales.This metric effectively captures the intricate noise and distortions inherent in the HSI data.Subsequently,the authors employ the Prophet model to forecast information within the low-quality bands,leveraging insights from neighbouring high-quality bands.To validate the effectiveness of the authors’proposed model,extensive experiments on three publicly available uncorrected datasets are conducted.In a head-to-head comparison,the framework against six state-ofthe-art band reconstruction algorithms including three spectral methods,two spatialspectral methods and one deep learning method is benchmarked.The authors’experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed bands.In addition,the authors assess the classification accuracy utilising these reconstructed bands.In various experiments,the results consistently affirm the efficacy of the authors’method in HSI quality assessment and band reconstruction.Notably,the authors’approach obviates the need for manually prefiltering of noisy bands.This comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.展开更多
Accurate mapping of wetlands is crucial for wetlands conservation, as well as for monitoring and assessing coastal resources and the environment. Multispectral(MSI) satellite image time series have rich temporal evolu...Accurate mapping of wetlands is crucial for wetlands conservation, as well as for monitoring and assessing coastal resources and the environment. Multispectral(MSI) satellite image time series have rich temporal evolution characteristics, which can reveal dynamic changes in surface cover and environmental conditions. However, due to the limited number of bands, the ability to express the difference of ground features is limited, resulting in an inability to capture surface objects' changes in the finer spectral range. Therefore,this paper proposed a dual-branch spatial-temporal spectral feature fusion network(Fusion-Former), which combined MSI time series data with hyperspectral(HSI) data to achieve accurate mapping of wetlands in Liaohe River Delta, China in 2022. Fusion-Former achieved an overall accuracy(OA) of 96.36% in the Liaohe River Delta wetland, significantly outperforming all benchmark methods.Experimental results demonstrate that utilizing the temporal phenological information from multi-temporal MSI and the fine-grained spatial-spectral features from HSI can effectively resolve the misclassification between spectrally similar vegetation and water bodies.Furthermore, a continuous improvement in accuracy was observed as the length of the input time series increased, underscoring the critical role of temporal information. Therefore, by integrating these complementary information sources, the proposed method enables the generation of accurate wetland maps to support decision-makers in formulating more precise conservation and management strategies.展开更多
In recent years,camouflage technology has evolved from single-spectral-band applications to multifunctional and multispectral implementations.Hyperspectral imaging has emerged as a powerful technique for target identi...In recent years,camouflage technology has evolved from single-spectral-band applications to multifunctional and multispectral implementations.Hyperspectral imaging has emerged as a powerful technique for target identification due to its capacity to capture both spectral and spatial information.The advancement of imaging spectroscopy technology has significantly enhanced reconnaissance capabilities,offering substantial advantages in camouflaged target classification and detection.However,the increasing spectral similarity between camouflaged targets and their backgrounds has significantly compromised detection performance in specific scenarios.Conventional feature extraction methods are often limited to single,shallow spectral or spatial features,failing to extract deep features and consequently yielding suboptimal classification accuracy.To address these limitations,this study proposes an innovative 3D-2D convolutional neural networks architecture incorporating depthwise separable convolution(DSC)and attention mechanisms(AM).The framework first applies dimensionality reduction to hyperspectral images and extracts preliminary spectral-spatial features.It then employs an alternating combination of 3D and 2D convolutions for deep feature extraction.For target classification,the LogSoftmax function is implemented.The integration of depthwise separable convolution not only enhances classification accuracy but also substantially reduces model parameters.Furthermore,the attention mechanisms significantly improve the network's ability to represent multidimensional features.Extensive experiments were conducted on a custom land-based hyperspectral image dataset.The results demonstrate remarkable classification accuracy:98.74%for grassland camouflage,99.13%for dead leaf camouflage and 98.94%for wild grass camouflage.Comparative analysis shows that the proposed framework is outstanding in terms of classification accuracy and robustness for camouflage target classification.展开更多
Taoren and Xingren are commonly used herbs in East Asian medicine with different medication functions but huge economic differences,and there are cases of adulterated sales in market transactions.An effective adultera...Taoren and Xingren are commonly used herbs in East Asian medicine with different medication functions but huge economic differences,and there are cases of adulterated sales in market transactions.An effective adulteration recognition based on hyperspectral technology and machine learning was designed as a non-destructive testing method in this paper.A hyperspectral dataset comprising 500 Taoren and 500 Xingren samples was established;six feature selection methods were considered in the modeling of radial basis function-support vector machine(RBF-SVM),whose interaction between the two optimization methods was further researched.Two mixed metaheuristics modeling methods,Mixed-PSO and Mixed-SA,were designed,which fused both band selection and hyperparameter optimization from two-stage into one with detailed process analysis.The metrics of this mixed model were improved by comparing with traditional two-stage method.The accuracy of Mixed-PSO was 89.2%in five-floods crossvalidation that increased 4.818%than vanilla RBF-SVM;the accuracy of Mixed-SA was 88.7%which could reach the same as the traditional two-stage method,but it only relied on 48 crux bands in full 100 bands in RBF-SVM model fitting.展开更多
基金funded by the National Natural Science Foundation of China(Nos.52474165 and 52522404)。
文摘Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly difficult mines and meet the requirements of environmental protection and safety regulations.It promotes the development of a circular economy in mines through the development of lowgrade resources and the resource utilization of waste,and extends the service life of mines.The mass concentration of solid content(abbreviated as“concentration”)is a critical parameter for CPB.However,discrepancies often arise between the on-site measurements and the pre-designed values due to factors such as groundwater inflow and segregation within the goaf,which cannot be evaluated after the solidification of CPB.This paper innovatively provides an in-situ non-destructive approach to identify the real concentration of CPB after curing for certain days using hyperspectral imaging(HSI)technology.Initially,the spectral variation patterns under different concentration conditions were investigated through hyperspectral scanning experiments on CPB samples.The results demonstrate that as the CPB concentration increases from 61wt%to 73wt%,the overall spectral reflectance gradually increases,with two distinct absorption peaks observed at 1407 and 1917 nm.Notably,the reflectance at 1407 nm exhibited a strong linear relationship with the concentration.Subsequently,the K-nearest neighbors(KNN)and support vector machine(SVM)algorithms were employed to classify and identify different concentrations.The study revealed that,with the KNN algorithm,the highest accuracy was achieved when K(number of nearest neighbors)was 1,although this resulted in overfitting.When K=3,the model displayed the optimal balance between accuracy and stability,with an accuracy of 95.03%.In the SVM algorithm,the highest accuracy of 98.24%was attained with parameters C(regularization parameter)=200 and Gamma(kernel coefficient)=10.A comparative analysis of precision,accuracy,and recall further highlighted that the SVM provided superior stability and precision for identifying CPB concentration.Thus,HSI technology offers an effective solution for the in-situ,non-destructive monitoring of CPB concentration,presenting a promising approach for optimizing and controlling CPB characteristic parameters.
基金supported in part by the Six Talent Peaks Project in Jiangsu Province under Grant 013040315in part by the China Textile Industry Federation Science and Technology Guidance Project under Grant 2017107+1 种基金in part by the National Natural Science Foundation of China under Grant 31570714in part by the China Scholarship Council under Grant 202108320290。
文摘The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.
基金supported by the Science and Technology Development Plan Project of Jilin Provincial Department of Science and Technology (No.20220203112S)the Jilin Provincial Department of Education Science and Technology Research Project (No.JJKH20210039KJ)。
文摘In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.
基金support from the National Natural Science Foundation of China(Grant Nos.52379103,52279103)the Natural Science Foundation of Shandong Province(Grant No.ZR2023YQ049).
文摘Imaging hyperspectral technology has distinctive advantages of non-destructive and non-contact measurement,and the integration of spectral and spatial data.These characteristics present new methodologies for intelligent geological sensing in tunnels and other underground engineering projects.However,the in situ acquisition and rapid classification of hyperspectral images in underground still faces great challenges,including the difficulty in obtaining uniform hyperspectral images and the complexity of deploying sophisticated models on mobile platforms.This study proposes an intelligent lithology identification method based on partition feature extraction of hyperspectral images.Firstly,pixel-level hyperspectral information from representative lithological regions is extracted and fused to obtain rock hyperspectral image partition features.Subsequently,an SG-SNV-PCA-DNN(SSPD)model specifically designed for optimizing rock hyperspectral data,performing spectral dimensionality reduction,and identifying lithology is integrated.In an experimental study involving 3420 hyperspectral images,the SSPD identification model achieved the highest accuracy in the testing set,reaching 98.77%.Moreover,the speed of the SSPD model was found to be 18.5%faster than that of the unprocessed model,with an accuracy improvement of 5.22%.In contrast,the ResNet-101 model,used for point-by-point identification based on non-partitioned features,achieved a maximum accuracy of 97.86%in the testing set.In addition,the partition feature extraction methods significantly reduce computational complexity.An objective evaluation of various models demonstrated that the SSPD model exhibited superior performance,achieving a precision(P)of 99.46%,a recall(R)of 99.44%,and F1 score(F1)of 99.45%.Additionally,a pioneering in situ detection work was carried out in a tunnel using underground hyperspectral imaging technology.
基金supported by Key Research&Development Program of Jiangsu Province in China(BE2020693)Major Project of Science and Technology of Anhui Province(201903a06020010)+1 种基金Joint Key Project of Science and Technology Innovation of Yangtze River Delta in Anhui Province(202004g01020009)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)。
文摘In this work,one-step growth models using hyperspectral imaging(HSI)(400-1000 nm)were successfully developed in order to estimate the microbial loads,minimum growth temperature(T_(min))and maximum specific growth rate(μ_(max))of Brochothrix thermosphacta in chilled beef at isothermal temperatures(4-25℃).Three different methods were compared for model development,particularly using(Model Ⅰ)the predicted microbial loads from partial least squares regression of the whole spectral variables;(Model Ⅱ)the selected spectral variables related to microbial loads;and(Model Ⅲ)the first principal scores of HSI spectra by principal component analysis.Consequently,Model Ⅰ showed the best ability to predict the microbial loads of B.thermosphacta,with the coefficient of determination(R_(v)^(2))and root mean square error in internal validation(RMSEV)of 0.921 and 0.498(lg(CFU/g)).The T_(min)(-12.32℃)andμmax can be well estimated with R^(2) and root mean square error(RMSE)of 0.971 and 0.276(lg(CFU/g)),respectively.The upward trend ofμmax with temperature was similar to that of the plate count method.HSI technique thus can be used as a simple method for one-step growth simulation of B.thermosphacta in chilled beef during storage.
文摘Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic spectrum.However,due to sensor limitations and imperfections during the image acquisition and transmission phases,noise is introduced into the acquired image,which can have a negative impact on downstream analyses such as classification,target tracking,and spectral unmixing.Noise in hyperspectral images(HSI)is modelled as a combination from several sources,including Gaussian/impulse noise,stripes,and deadlines.An HSI restoration method for such a mixed noise model is proposed.First,a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data as well as the sparse/impulse noise levels.Second,a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image gradients.Third,to model the sparse nature of impulse noise,anℓ_(1)−norm over the impulse noise gradient is used.Because the proposed methodology employs two distinct priors,the authors refer to it as the hyperspectral dual prior(HySpDualP)denoiser.To the best of authors'knowledge,this joint optimisation framework is the first attempt in this direction.To handle the non-smooth and nonconvex nature of the generalℓ_(p)−norm-based regularisation term,a generalised shrinkage/thresholding(GST)solver is employed.Finally,an efficient split-Bregman approach is used to solve the resulting optimisation problem.Experimental results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’proposed model outperforms state-of-the-art methods,both visually and in terms of various image quality assessment metrics.
基金supported by the Corps Leading Talents Program,China(2023YZ01)the Tianshan Talent Training Program,China(2023TS05)the Crop Smart Production Innovation Team,China(2023TD01).
文摘Potassium(K)is a highly mobile nutrient element that continuously adjusts its demand strategy among and within cotton leaves through redistribution,indirectly leading to variations in the leaf potassium content(LKC,%)at different leaf positions.However,due to the interaction between light and leaf age,leaf sensitivity to this change varies at different positions,including the reflection and absorption of the spectrum.Selecting the optimal leaf position for monitoring is a crucial factor in the rapid and accurate evaluation of cotton LKC using spectral remote sensing technology.Therefore,this study proposes a comprehensive multi-leaf position estimation model based on the vertical distribution characteristics of LKC from top to bottom,aiming to achieve an accurate estimation of cotton LKC and optimize the strategy for selecting the monitored leaf position.Between 2020 and 2021,we collected hyperspectral imaging data of the main stem leaves at different positions from top to bottom(Li,i=1,2,3,...,n)during the cotton budding,flowering,and boll-setting stages.Vertical distribution characteristics,sensitivity differences,and spectral correlations of LKC at different leaf positions were investigated.Additionally,the optimal range of the dominant leaf position for monitoring was determined.Partial least squares regression(PLSR),random forest regression(RFR),support vector machine regression(SVR),and the entropy weight method(EWM)were employed to develop LKC estimation models for single-and multi-leaf positions.The results showed a vertical heterogeneous distribution of cotton LKC,with LKC initially increasing and then gradually decreasing from top to bottom;the average LKC of cotton reached its maximum value at the flowering stage.The upper leaf position demonstrated greater sensitivity to K and exhibited a stronger correlation with the spectrum.The selected dominant leaf positions for the three growth stages were L1-L5,L1-L4,and L1-L2,respectively.Based on the dominant leaf position monitoring range,the optimal single leaf position models for estimating LKC during the three growth stages were PLSR-L4,PLSR-L1,and SVR-L2,with the coefficient of determination of the validation set(R2val)being 0.786,0.580,and 0.768,and the root-mean-square error of the validation set(RMSEval)being 0.168,0.197,and 0.191,respectively.The multi-leaf position LKC estimation model was constructed by EWM with R2val being 0.887,0.728,and 0.703,and RMSEval being 0.134,0.172,and 0.209,respectively.In contrast,the newly developed multi-leaf position comprehensive estimation model yielded superior results,improving the model’s stability based on high accuracy,especially during the budding and flowering stages.These findings hold significant importance for investigating cotton LKC spectral models and selecting suitable leaf positions for field monitoring.
基金supported by the China Agriculture Research System(CARS-15-22)the Laboratory of Lingnan Modern Agriculture Project,China(NT2021009)+2 种基金the Key-Area Research and Development Program ofGuangdong Province,China(2019B020214003)the Guangdong Technical System of Peanut and Soybean Industry,China(2019KJ136-05)the 111 Project,China(D18019)。
文摘Quantitative inversion is a major topic in remote sensing science.The development of visible light-based hyperspectral reconstruction techniques has opened novel prospects for low-cost,high-precision remote sensing inversion in agriculture.The aim of this study was to assess the effectiveness of hyperspectral reconstruction technology in agricultural remote sensing applications.Hyperspectral images were reconstructed using the MST++hyperspectral reconstruction model and compared with the original visible light images in terms of their correlations with physiological parameters,the accuracy of single-feature modeling,and the accuracy of combined feature modeling.The results showed that compared to the visible light image,the reconstructed data exhibited a stronger correlation with the measured physiological parameters,and the accuracy was improved for both the single feature and combined feature inversion modes.However,compared to multispectral sensors,hyperspectral reconstruction provided limited improvement of the inversion model accuracy.The results suggest that for physiological parameters that are not easy to observe directly,deep mining of features in visible light data through hyperspectral reconstruction technology can improve the accuracy of the inversion model.However,appropriate feature selection and simple models are more suitable for the remote sensing inversion task of traditional agronomic plot experiments.To strengthen the application of hyperspectral reconstruction technology in agricultural remote sensing,further development is necessary with broader wavelength ranges and more diverse agricultural scenarios.
文摘BACKGROUND Hyperspectral imaging(HSI)offers useful information on organ quality and has already been successfully used in kidney and liver transplantation to assess transplanted organs.Up to now,there is no case report in the literature describing HSI for quality assessment of a machine perfused donor liver.The allocated liver from a 49-year-old female donor(161 cm,70 kg)was perfused with the OrganOx®normothermic machine perfusion system in the recommended way.Organ quality assessment was performed based on laboratory values at defined time points.In addition,the final evaluation of the liver comprised macroscopic findings and HSI of each liver segment.After discarding the organ,biopsies were taken from each segment and correlated with the results of the HSI.CASE SUMMARY The donor liver’s size(29 cm×17 cm×11 cm)and weight of 2180 g posed challenges for adequate placement within the organ container.Baseline biopsy of the liver revealed no evidence of fibrosis,steatosis or inflammation.An hour after perfusion start,measurements of the perfusate indicated a pH of 7.18,a glucose level of 404 mg/dL,and a lactate level of 1.7 mmol/L.Throughout perfusion,a significant decline in glucose levels began at the fourth hour,reaching a nadir of 20 mg/dL after eight hours.Concurrently,lactate levels steadily rose,peaking at 4.9 mmol/L after the total perfusion time of 12 hours.Macroscopic alterations(signs of congestion and reduced blood circulation)on the liver’s surface were noted,particularly pronounced in segments 2,3,and 8.HSI of these areas unveiled significant reduced oxygenation.Consequently,based on all these observations,the decision was made to discard the organ.Histological examination of the altered regions revealed congestion,necrotic changes,and dissociation of CONCLUSION This case report describes the integration of HSI in the decision making of the decline of a 49-year-old machine perfused donor liver.HSI offered useful information concerning the tissue morphology and graft viability and could therefore be a useful additional tool in assessing donor liver quality before transplantation.
基金supported in part by the National Natural Science Foundations of China(No.61801214)the Postgraduate Research Practice Innovation Program of NUAA(No.xcxjh20231504)。
文摘Hyperspectral image(HSI)classification is crucial for numerous remote sensing applications.Traditional deep learning methods may miss pixel relationships and context,leading to inefficiencies.This paper introduces the spectral band graph convolutional and attention-enhanced CNN joint network(SGCCN),a novel approach that harnesses the power of spectral band graph convolutions for capturing long-range relationships,utilizes local perception of attention-enhanced multi-level convolutions for local spatial feature and employs a dynamic attention mechanism to enhance feature extraction.The SGCCN integrates spectral and spatial features through a self-attention fusion network,significantly improving classification accuracy and efficiency.The proposed method outperforms existing techniques,demonstrating its effectiveness in handling the challenges associated with HSI data.
基金supported by the‘Pioneer’and‘Leading Goose’R&D Program of Zhejiang(Grant No.2023C02018)Zhejiang Provincial Natural Science Foundation of China(Grant No.LTGN23D010002)+2 种基金National Natural Science Foundation of China(Grant No.42371385)Funds of the Natural Science Foundation of Hangzhou(Grant No.2024SZRYBD010001)Nanxun Scholars Program of ZJWEU(Grant No.RC2022010755).
文摘Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-band spectra,hyperspectral technology has become a crucial tool to monitor crop diseases using remote sensing.However,existing continuous wavelet analysis(CWA)methods suffer from feature redundancy issues,while the continuous wavelet projection algorithm(CWPA),an optimization approach for feature selection,has not been fully validated to monitor plant diseases.This study utilized rice bacterial leaf blight(BLB)as an example by evaluating the performance of four wavelet basis functions-Gaussian2,Mexican hat,Meyer,andMorlet-within theCWAandCWPAframeworks.Additionally,the classification models were constructed using the k-nearest neighbors(KNN),randomforest(RF),and Naïve Bayes(NB)algorithms.The results showed the following:(1)Compared to traditional CWA,CWPA significantly reduced the number of required features.Under the CWPA framework,almost all the model combinations achieved maximum classification accuracy with only one feature.In contrast,the CWA framework required three to seven features.(2)Thechoice of wavelet basis functions markedly affected the performance of themodel.Of the four functions tested,the Meyer wavelet demonstrated the best overall performance in both the CWPA and CWA frameworks.(3)Under theCWPAframework,theMeyer-KNNandMeyer-NBcombinations achieved the highest overall accuracy of 93.75%using just one feature.In contrast,under the CWA framework,the CWA-RF combination achieved comparable accuracy(93.75%)but required six features.This study verified the technical advantages of CWPA for monitoring crop diseases,identified an optimal wavelet basis function selection scheme,and provided reliable technical support to precisely monitor BLB in rice(Oryza sativa).Moreover,the proposed methodological framework offers a scalable approach for the early diagnosis and assessment of plant stress,which can contribute to improved accuracy and timeliness when plant stress is monitored.
基金supported by the Fundamental Research Funds for the Provincial Universities of Zhejiang (No.GK249909299001-036)National Key Research and Development Program of China (No. 2023YFB4502803)Zhejiang Provincial Natural Science Foundation of China (No.LDT23F01014F01)。
文摘Due to the limitations of existing imaging hardware, obtaining high-resolution hyperspectral images is challenging. Hyperspectral image super-resolution(HSI SR) has been a very attractive research topic in computer vision, attracting the attention of many researchers. However, most HSI SR methods focus on the tradeoff between spatial resolution and spectral information, and cannot guarantee the efficient extraction of image information. In this paper, a multidimensional features network(MFNet) for HSI SR is proposed, which simultaneously learns and fuses the spatial,spectral, and frequency multidimensional features of HSI. Spatial features contain rich local details,spectral features contain the information and correlation between spectral bands, and frequency feature can reflect the global information of the image and can be used to obtain the global context of HSI. The fusion of the three features can better guide image super-resolution, to obtain higher-quality high-resolution hyperspectral images. In MFNet, we use the frequency feature extraction module(FFEM) to extract the frequency feature. On this basis, a multidimensional features extraction module(MFEM) is designed to learn and fuse multidimensional features. In addition, experimental results on two public datasets demonstrate that MFNet achieves state-of-the-art performance.
基金the Research Training Program International(RTPi)scholarship from Commonwealth Australiathe top-up scholarship provided by SureNut Ltd.SureNut Ltd.for supplying all the almonds used in this study.
文摘Aflatoxin B1(AFB1)is a toxic fungal metabolite that contaminates almonds from cultivation to harvesting.It leads to chronic health problems and significant economic loss to the producers.Therefore,a fast and non-invasive detection technique is crucial for safeguarding food safety by swiftly identifying and eliminating contaminated almonds from the supply chain.Hyperspectral imaging has been explored as a potential non-destructive technology for detecting AFB1.However,the diverse geometries of almonds present a significant challenge on acquired images,thereby impacting the accuracy of the developed prediction and classification models.This study investigates the effectiveness of short-wave infrared(SwIR)hyperspectral imaging combined with deep learning for detecting AFB1 in almonds of varying geometries.Initially,partial least squares regression(PLSR)and support vector machine(SvM)regression models were evaluated for quantification,while SVM and quadratic discriminant analysis(QDA)classifiers were applied for classification.The results indicated that spectral responses varied with almond thickness,making quantification models unreliable for industrial applications.The Competitive Adaptive Reweighted Sampling(CARS)algorithm was employed to identify key spectral features for developing multi-spectral AFB1 classification models to evaluate the feasibility of high-speed,accurate in-line detection.The deep learning approach significantly outperformed traditional machine learning models,with the pre-trained Inception V3 network achieving a cross-validation accuracy of 84.82%,an F1-score of 0.8522,and an area under curve of 0.893.These findings highlight the superiority of deep learning-based hyperspectral imaging for accurate and reliable AFB1 detection in almonds with diverse shapes and thicknesses.
基金supported by the National Natural Science Foundation of China(No.62071323).
文摘In hyperspectral image classification(HSIC),accurately extracting spatial and spectral information from hyperspectral images(HSI)is crucial for achieving precise classification.However,due to low spatial resolution and complex category boundary,mixed pixels containing features from multiple classes are inevitable in HSIs.Additionally,the spectral similarity among different classes challenge for extracting distinctive spectral features essential for HSIC.To address the impact of mixed pixels and spectral similarity for HSIC,we propose a central-pixel guiding sub-pixel and sub-channel convolution network(CP-SPSC)to extract more precise spatial and spectral features.Firstly,we designed spatial attention(CP-SPA)and spectral attention(CP-SPE)informed by the central pixel to effectively reduce spectral interference of irrelevant categories in the same patch.Furthermore,we use CP-SPA to guide 2D sub-pixel convolution(SPConv2d)to capture spatial features finer than the pixel level.Meanwhile,CP-SPE is also utilized to guide 1D sub-channel con-volution(SCConv1d)in selecting more precise spectral channels.For fusing spatial and spectral information at the feature-level,the spectral feature extension transformation module(SFET)adopts mirror-padding and snake permutation to transform 1D spectral information of the center pixel into 2D spectral features.Experiments on three popular datasets demonstrate that ours out-performs several state-of-the-art methods in accuracy.
文摘Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep learning.Firstly, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based algorithms.In addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.
基金National Natural Science Foundation Major Project of China,Grant/Award Number:42192580Guangdong Province Key Construction Discipline Scientific Research Ability Promotion Project,Grant/Award Number:2022ZDJS015。
文摘In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land mapping.This study presents an innovative framework for assessing HSI band quality and reconstructing the low-quality bands,based on the Prophet model.By introducing a comprehensive quality metric to start,the authors approach factors in both spatial and spectral characteristics across local and global scales.This metric effectively captures the intricate noise and distortions inherent in the HSI data.Subsequently,the authors employ the Prophet model to forecast information within the low-quality bands,leveraging insights from neighbouring high-quality bands.To validate the effectiveness of the authors’proposed model,extensive experiments on three publicly available uncorrected datasets are conducted.In a head-to-head comparison,the framework against six state-ofthe-art band reconstruction algorithms including three spectral methods,two spatialspectral methods and one deep learning method is benchmarked.The authors’experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed bands.In addition,the authors assess the classification accuracy utilising these reconstructed bands.In various experiments,the results consistently affirm the efficacy of the authors’method in HSI quality assessment and band reconstruction.Notably,the authors’approach obviates the need for manually prefiltering of noisy bands.This comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.
基金Under the auspices of the National Natural Science Foundation of China(No.42471380,42325104,42101350)。
文摘Accurate mapping of wetlands is crucial for wetlands conservation, as well as for monitoring and assessing coastal resources and the environment. Multispectral(MSI) satellite image time series have rich temporal evolution characteristics, which can reveal dynamic changes in surface cover and environmental conditions. However, due to the limited number of bands, the ability to express the difference of ground features is limited, resulting in an inability to capture surface objects' changes in the finer spectral range. Therefore,this paper proposed a dual-branch spatial-temporal spectral feature fusion network(Fusion-Former), which combined MSI time series data with hyperspectral(HSI) data to achieve accurate mapping of wetlands in Liaohe River Delta, China in 2022. Fusion-Former achieved an overall accuracy(OA) of 96.36% in the Liaohe River Delta wetland, significantly outperforming all benchmark methods.Experimental results demonstrate that utilizing the temporal phenological information from multi-temporal MSI and the fine-grained spatial-spectral features from HSI can effectively resolve the misclassification between spectrally similar vegetation and water bodies.Furthermore, a continuous improvement in accuracy was observed as the length of the input time series increased, underscoring the critical role of temporal information. Therefore, by integrating these complementary information sources, the proposed method enables the generation of accurate wetland maps to support decision-makers in formulating more precise conservation and management strategies.
文摘In recent years,camouflage technology has evolved from single-spectral-band applications to multifunctional and multispectral implementations.Hyperspectral imaging has emerged as a powerful technique for target identification due to its capacity to capture both spectral and spatial information.The advancement of imaging spectroscopy technology has significantly enhanced reconnaissance capabilities,offering substantial advantages in camouflaged target classification and detection.However,the increasing spectral similarity between camouflaged targets and their backgrounds has significantly compromised detection performance in specific scenarios.Conventional feature extraction methods are often limited to single,shallow spectral or spatial features,failing to extract deep features and consequently yielding suboptimal classification accuracy.To address these limitations,this study proposes an innovative 3D-2D convolutional neural networks architecture incorporating depthwise separable convolution(DSC)and attention mechanisms(AM).The framework first applies dimensionality reduction to hyperspectral images and extracts preliminary spectral-spatial features.It then employs an alternating combination of 3D and 2D convolutions for deep feature extraction.For target classification,the LogSoftmax function is implemented.The integration of depthwise separable convolution not only enhances classification accuracy but also substantially reduces model parameters.Furthermore,the attention mechanisms significantly improve the network's ability to represent multidimensional features.Extensive experiments were conducted on a custom land-based hyperspectral image dataset.The results demonstrate remarkable classification accuracy:98.74%for grassland camouflage,99.13%for dead leaf camouflage and 98.94%for wild grass camouflage.Comparative analysis shows that the proposed framework is outstanding in terms of classification accuracy and robustness for camouflage target classification.
基金Supported by the Natural Science Foundation of Heilongjiang Province(LH2020C003)。
文摘Taoren and Xingren are commonly used herbs in East Asian medicine with different medication functions but huge economic differences,and there are cases of adulterated sales in market transactions.An effective adulteration recognition based on hyperspectral technology and machine learning was designed as a non-destructive testing method in this paper.A hyperspectral dataset comprising 500 Taoren and 500 Xingren samples was established;six feature selection methods were considered in the modeling of radial basis function-support vector machine(RBF-SVM),whose interaction between the two optimization methods was further researched.Two mixed metaheuristics modeling methods,Mixed-PSO and Mixed-SA,were designed,which fused both band selection and hyperparameter optimization from two-stage into one with detailed process analysis.The metrics of this mixed model were improved by comparing with traditional two-stage method.The accuracy of Mixed-PSO was 89.2%in five-floods crossvalidation that increased 4.818%than vanilla RBF-SVM;the accuracy of Mixed-SA was 88.7%which could reach the same as the traditional two-stage method,but it only relied on 48 crux bands in full 100 bands in RBF-SVM model fitting.
