A surface soil moisture model with improved spatial resolution was developed using remotely sensed apparent thermal inertia（ATI）.The model integrates the surface temperature derived from TM/ETM＋ image and the mean ...A surface soil moisture model with improved spatial resolution was developed using remotely sensed apparent thermal inertia（ATI）.The model integrates the surface temperature derived from TM/ETM＋ image and the mean surface temperature from MODIS images to improve the spatial resolution of soil temperature difference based on the heat conduction equation,which is necessary to calculate the ATI.Consequently,the spatial resolution of ATI and SMC can be enhanced from 1 km to 120 m（TM） or 60m（ETM＋）.Moreover,the enhanced ATI has a much stronger correlation coefficient（R^2） with SMC（0.789） than the surface reflectance（0.108） or the ATI derived only from MODIS images（0.264）.Based on the regression statistics of the field SMC measurement and enhanced ATI,a linear regression model with an RMS error of 1.90%was found.展开更多
Impervious surfaces are the result of urbanization that can be explicitly quantified, managed and controlled at each stage of land development. It is a very useful environmental indicator that can be used to measure t...Impervious surfaces are the result of urbanization that can be explicitly quantified, managed and controlled at each stage of land development. It is a very useful environmental indicator that can be used to measure the impacts of urbanization on surface runoff, water quality, air quality, biodiversity and rnicroclimate. Therefore, accurate estimation of impervious surfaces is critical for urban environmental monitoring, land management, decision-making and urban planning. Many approaches have been developed to estimate surface imperviousness, using remotely sensed data with various spatial resolutions. However, few studies, have investigated the effects of spatial resolution on estimating surface imperviousness. We compare medium-resolution Landsat data with high-resolution SPOT images to quantify the imperviousness in Beijing, China. The results indicated that the overall 91% accuracy of estimates of imperviousness based on TM data was considerably higher than the 81% accuracy of the SPOT data. The higher resolution SPOT data did not always predict the imperviousness of the land better than the TM data. At the whole city level, the TM data better predicts the percentage cover of impervious surfaces. At the sub-city level, however, the ring belts from the central core to the urban-rural peripheral, the SPOT data may better predict the imperviousness. These results highlighted the need to combine multiple resolution data to quantify the percentage of imperviousness, as higher resolution data do not necessarily lead to more accurate estimates. The methodology and results in this study can be utilized to identify the most suitable remote sensing data to quickly and efficiently extract the pattern of the impervious land, which could provide the base for further study on many related urban environmental problems.展开更多
Ultrafast imaging tools are of great importance for determining the dynamic density distribution in high energy density(HED)matter.In this work,we designed a high energy electron radiography(HEER)system based on a lin...Ultrafast imaging tools are of great importance for determining the dynamic density distribution in high energy density(HED)matter.In this work,we designed a high energy electron radiography(HEER)system based on a linear electron accelerator to evaluate its capability for imaging HED matter.40 MeV electron beams were used to image an aluminum target to study the density resolution and spatial resolution of HEER.The results demonstrate a spatial resolution of tens of micrometers.The interaction of the beams with the target and the beam transport of the transmitted electrons are further simulated with EGS5 and PARMELA codes,with the results showing good agreement with the experimental resolution.Furthermore,the experiment can be improved by adding an aperture at the Fourier plane.展开更多
Albacore tuna(Thunnus alalunga)is one of the target species of tuna longline fishing,and waters near the Cook Islands are a vital albacore tuna fishing ground.Marine environmental data are usually presented with diffe...Albacore tuna(Thunnus alalunga)is one of the target species of tuna longline fishing,and waters near the Cook Islands are a vital albacore tuna fishing ground.Marine environmental data are usually presented with different spatial resolutions,which leads to different results in tuna fishery prediction.Study on the impact of different spatial resolutions on the prediction accuracy of albacore tuna fishery to select the best spatial resolution can contribute to better management of albacore tuna resources.The nominal catch per unit effort(CPUE)of albacore tuna is calculated according to vessel monitor system(VMS)data collected from Chinese distantwater fishery enterprises from January 1,2017 to May 31,2021.A total of 26 spatiotemporal and environmental factors,including temperature,salinity,dissolved oxygen of 0–300 m water layer,chlorophyll-a concentration in the sea surface,sea surface height,month,longitude,and latitude,were selected as variables.The temporal resolution of the variables was daily and the spatial resolutions were set to be 0.5°×0.5°,1°×1°,2°×2°,and 5°×5°.The relationship between the nominal CPUE and each individual factor was analyzed to remove the factors irrelavant to the nominal CPUE,together with a multicollinearity diagnosis on the factors to remove factors highly related to the other factors within the four spatial resolutions.The relationship models between CPUE and spatiotemporal and environmental factors by four spatial resolutions were established based on the long short-term memory(LSTM)neural network model.The mean absolute error(MAE)and root mean square error(RMSE)were used to analyze the fitness and accuracy of the models,and to determine the effects of different spatial resolutions on the prediction accuracy of the albacore tuna fishing ground.The results show the resolution of 1°×1°can lead to the best prediction accuracy,with the MAE and RMSE being 0.0268 and 0.0452 respectively,followed by 0.5°×0.5°,2°×2°and 5°×5°with declining prediction accuracy.The results suggested that 1)albacore tuna fishing ground can be predicted by LSTM;2)the VMS records the data in detail and can be used scientifically to calculate the CPUE;3)correlation analysis,and multicollinearity diagnosis are necessary to improve the prediction accuracy of the model;4)the spatial resolution should be 1°×1°in the forecast of albacore tuna fishing ground in waters near the Cook Islands.展开更多
Accurate information on the location and magnitude of vegetation change in scenic areas can guide the configuration of tourism facilities and the formulation of vegetation protection measures.High spatial resolution r...Accurate information on the location and magnitude of vegetation change in scenic areas can guide the configuration of tourism facilities and the formulation of vegetation protection measures.High spatial resolution remote sensing images can be used to detect subtle vegetation changes.The major objective of this study was to map and quantify forest vegetation changes in a national scenic location,the Purple Mountains of Nanjing,China,using multi-temporal cross-sensor high spatial resolution satellite images to identify the main drivers of the vegetation changes and provide a reference for sustainable management.We used Quickbird images acquired in 2004,IKONOS images acquired in 2009,and WorldView2 images acquired in 2015.Four pixel-based direct change detection methods including the normalized difference vegetation index difference method,multi-index integrated change analysis(MIICA),principal component analysis,and spectral gradient difference analysis were compared in terms of their change detection performances.Subsequently,the best pixel-based detection method in conjunction with object-oriented image analysis was used to extract subtle forest vegetation changes.An accuracy assessment using the stratified random sampling points was conducted to evaluate the performance of the change detection results.The results showed that the MIICA method was the best pixel-based change detection method.And the object-oriented MIICA with an overall accuracy of 0.907 and a kappa coefficient of 0.846 was superior to the pixel-based MIICA.From 2004 to 2009,areas of vegetation gain mainly occurred around the periphery of the study area,while areas of vegetation loss were observed in the interior and along the boundary of the study area due to construction activities,which contributed to 79%of the total area of vegetation loss.During 2009–2015,the greening initiatives around the construction areas increased the forest vegetation coverage,accounting for 84%of the total area of vegetation gain.In spite of this,vegetation loss occurred in the interior of the Purple Mountains due to infrastructure development that caused conversion from vegetation to impervious areas.We recommend that:(1)a local multi-agency team inspect and assess law enforcement regarding natural resource utilization;and(2)strengthen environmental awareness education.展开更多
Spatial resolution and image-processing methods for full-field X-ray fluorescence(FF-XRF)imaging using X-ray pinhole cameras were studied using Geant4simulations with different geometries and algorithms for image reco...Spatial resolution and image-processing methods for full-field X-ray fluorescence(FF-XRF)imaging using X-ray pinhole cameras were studied using Geant4simulations with different geometries and algorithms for image reconstruction.The main objectives were:(1)calculating the quantum efficiency curves of specific cameras,(2)studying the relationships between the spatial resolution and the pinhole diameter,magnification,and camera binning value,and(3)comparing image-processing methods for pinhole camera systems.Several results were obtained using a point and plane source as the X-ray fluorescence emitter and an array of 100×100 silicon pixel detectors as the X-ray camera.The quantum efficiency of a back-illuminated deep depletion(BI-DD)structure was above 30%for the XRF energies in the 0.8–9 keV range,with the maximum of 93.7%at 4 keV.The best spatial resolution of the pinhole camera was 24.7μm and 31.3 lp/mm when measured using the profile function of the point source,with the diameter of 20μm,magnification of 3.16,and camera bin of 1.A blind deconvolution algorithm with Gaussian filtering performed better than the Wiener filter and Richardson iterative methods on FF-XRF images,with the signal-to-noise ratio of 7.81 dB and improved signalto-noise ratio of 7.24 dB at the diameter of 120μm,magnification of 1.0,and camera bin of 1.展开更多
Remote sensing has played a pivotal role in our understanding of the geometry of dykes and dyke swarms on Earth,Venus and Mars(West and Ernst,1991;Mege and Masson,1995;Ernst et al.,2005).Since the 1970’s
A spatial resolution effect of remote sensing bathymetry is an important scientific problem. The in situ measured water depth data and images of Dongdao Island are used to study the effect of water depth inversion fro...A spatial resolution effect of remote sensing bathymetry is an important scientific problem. The in situ measured water depth data and images of Dongdao Island are used to study the effect of water depth inversion from different spatial resolution remote sensing images. The research experiments are divided into five groups including Quick Bird and World View-2 remote sensing images with their original spatial resolution（2.4/2.0 m）and four kinds of reducing spatial resolution（4, 8, 16 and 32 m）, and the water depth control and checking points are set up to carry out remote sensing water depth inversion. The experiment results indicate that the accuracy of the water depth remote sensing inversion increases first as the spatial resolution decreases from 2.4/2.0 to 4, 8 and16 m. And then the accuracy decreases along with the decreasing spatial resolution. When the spatial resolution of the image is 16 m, the inversion error is minimum. In this case, when the spatial resolution of the remote sensing image is 16 m, the mean relative errors（MRE） of Quick Bird and World View-2 bathymetry are 21.2% and 13.1%,compared with the maximum error are decreased by 14.7% and 2.9% respectively; the mean absolute errors（MAE） are 2.0 and 1.4 m, compared with the maximum are decreased by 1.0 and 0.5 m respectively. The results provide an important reference for the selection of remote sensing data in the study and application of the remote sensing bathymetry.展开更多
[ Objective] The study aimed to improve methods of monitoring Karst Rocky Desertification （KRD） control projects and increase the working efficiency. [Method] Based on remote sensing images with medium and high spat...[ Objective] The study aimed to improve methods of monitoring Karst Rocky Desertification （KRD） control projects and increase the working efficiency. [Method] Based on remote sensing images with medium and high spatial resolution, KRD control projects in Disi River basin in Puan County were monitored, that is, information of the project construction in the study area was extracted using supervised classification and hu- man-computer interactive interpretation, and the monitoring results were testified with the aid of GPS. [Result] It was feasible to monitor KRD con- trol projects in Disi River basin based on remote sensing images with medium and high resolution, and the monitoring accuracy was satisfactory, reaching above 80% or 90%, so the method is worthy of popularizing. [ Conclusion] Remote sensing images with medium and high resolution can be used to monitor other KRD control Droiects.展开更多
Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing da...Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing data processing is time-consuming and resource-intensive,and significantly hampers the efficiency and timeliness of soil moisture mapping.Due to the high-speed computing capabilities of remote sensing cloud platforms,a High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF)based on the Google Earth Engine(GEE)platform was developed in this study.The functions of the HSRSMEF include research area and input datasets customization,radar speckle noise filtering,optical-radar image spatio-temporal matching,soil moisture retrieving,soil moisture visualization and exporting.This paper tested the performance of HSRSMEF by combining Sentinel-1,Sentinel-2 images and insitu soil moisture data in the central farmland area of Jilin Province,China.Reconstructed Normalized Difference Vegetation Index(NDVI)based on the Savitzky-Golay algorithm conforms to the crop growth cycle,and its correlation with the original NDVI is about 0.99(P<0.001).The soil moisture accuracy of the random forest model(R 2=0.942,RMSE=0.013 m3/m3)is better than that of the water cloud model(R 2=0.334,RMSE=0.091 m3/m3).HSRSMEF transfers time-consuming offline operations to cloud computing platforms,achieving rapid and simplified high spatial resolution soil moisture mapping.展开更多
In recent years,gas electron multiplier(GEM)neutron detectors have been developing towards high spatial resolution and high dynamic counting range.We propose a novel concept of an Al stopping layer to enable the detec...In recent years,gas electron multiplier(GEM)neutron detectors have been developing towards high spatial resolution and high dynamic counting range.We propose a novel concept of an Al stopping layer to enable the detector to achieve sub-millimeter(sub-mm)spatial resolution.The neutron conversion layer is coated with the Al stopping layer to limit the emission angle of ions into the drift region.The short track projection of ions is obtained on the signal readout board,and the detector would get good spatial resolution.The spatial resolutions of the GEM neutron detector with the Al stopping layer are simulated and optimized based on Geant4 Garfield Interface.The spatial resolution of the detector is 0.76 mm and the thermal neutron detection efficiency is about 0.01%when the Al stopping layer is 3.0μm thick,the drift region is 2 mm thick,the strip pitch is 600μm,and the digital readout is employed.Thus,the GEM neutron detector with a simple detector structure and a fast readout mode is developed to obtain a high spatial resolution and high dynamic counting range.It could be used for the direct measurement of a high-flux neutron beam,such as Bragg transmission imaging,very small-angle scattering neutron detection and neutron beam diagnostic.展开更多
A novel methodology to quantify the spatial resolution in 2-D seismic surface wave tomographic problems is proposed in this study. It is based on both the resolving kernels computed via full resolution matrix and the ...A novel methodology to quantify the spatial resolution in 2-D seismic surface wave tomographic problems is proposed in this study. It is based on both the resolving kernels computed via full resolution matrix and the concept of Full Width at Half Maximum (FWHM) of a Gaussian function. This method allows estimating quantitatively the spatial resolution at any cell of a gridded area. It was applied in the northeastern Brazil and the estimated spatial resolution range is in agreement with all previous surface wave investigations in the South America continent.展开更多
Land use and land cover(LULC)has undergone drastic changes with the rapid growth of the global population,economic development,and the expansion of agricultural activities.However,the uncertainty of classification alg...Land use and land cover(LULC)has undergone drastic changes with the rapid growth of the global population,economic development,and the expansion of agricultural activities.However,the uncertainty of classification algorithms and image resolution based on satellite data for land cover mapping,particularly cropland cover mapping,needs to be investigated sufficiently.In this study,the influence of different spatial-resolution images on classification results was explored by comparing the differences between four machine learning algorithms for LULC mapping.The classification results of this model were also compared with existing global land cover datasets to determine whether the model was capable of producing reliable results.According to the results of this study,the random forest(RF)classifier outperformed the support vector machine(SVM),decision tree(DT),and artificial neural network(ANN)with an overall accuracy(OA)and kappa coefficient of 81.99%and 0.78,respectively.However,SVM and ANN showed greater accuracy on the water class and unused land class,respectively.With increasing spatial resolution,RF’s accuracy increased initially and then decreased when classifying images with five different spatial resolutions(30 m,16 m,10 m,8 m,and 2 m).In particular,with an OA of 82.54%and a kappa coefficient of 0.78,RF performed the best on images with 8 m resolution.Additionally,the RF-based image with 8 m resolution produced a higher OA of 0.88 for cropland.Topography is the main factor that determines the classification performance of different-resolution images.The classification accuracies of RF10 m and RF30 m(10 m and 30 m resolution images,respectively,using RF)were higher(OAs of 93.59%and 94.59%,respectively)than those of the global land cover dataset(LC10 m and LC30 m,land cover images with 10 m and 30 m resolution,respectively),whose high-resolution images showed more details of the land cover.The results of this study highlight that classification algorithms and image resolution are the sources of uncertainty for land mapping.Obtaining reliable land cover mapping requires the use of appropriate classification algorithms and spatial resolution.With these results,it will be possible to develop a national land monitoring system and basic ecological climate models using LULC.展开更多
The resolution of most spatially resolved transcriptomic technologies usually cannot attain the single-cell level,limiting their applications in biological discoveries.Here,we introduce ImSpiRE,an image feature-aided ...The resolution of most spatially resolved transcriptomic technologies usually cannot attain the single-cell level,limiting their applications in biological discoveries.Here,we introduce ImSpiRE,an image feature-aided spatial resolution enhancement method for in situ capturing spatial transcriptome.Taking the information stored in histological images,ImSpiRE solves an optimal transport problem to redistribute the expression profiles of spots to construct new transcriptional profiles with enhanced resolution,together with extending the gene expression profiles into unmeasured regions.Applications to multiple datasets confirm that ImSpiRE can enhance spatial resolution to the subspot level while contributing to the discovery of tissue domains,signaling communication patterns,and spatiotemporal characterization.展开更多
The efficacy of vegetation dynamics simulations in offline land surface models(LSMs)largely depends on the quality and spatial resolution of meteorological forcing data.In this study,the Princeton Global Meteorologica...The efficacy of vegetation dynamics simulations in offline land surface models(LSMs)largely depends on the quality and spatial resolution of meteorological forcing data.In this study,the Princeton Global Meteorological Forcing Data(PMFD)and the high spatial resolution and upscaled China Meteorological Forcing Data(CMFD)were used to drive the Simplified Simple Biosphere model version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics(SSiB4/TRIFFID)and investigate how meteorological forcing datasets with different spatial resolutions affect simulations over the Tibetan Plateau(TP),a region with complex topography and sparse observations.By comparing the monthly Leaf Area Index(LAI)and Gross Primary Production(GPP)against observations,we found that SSiB4/TRIFFID driven by upscaled CMFD improved the performance in simulating the spatial distributions of LAI and GPP over the TP,reducing RMSEs by 24.3%and 20.5%,respectively.The multi-year averaged GPP decreased from 364.68 gC m^(-2)yr^(-1)to 241.21 gC m^(-2)yr^(-1)with the percentage bias dropping from 50.2%to-1.7%.When using the high spatial resolution CMFD,the RMSEs of the spatial distributions of LAI and GPP simulations were further reduced by 7.5%and 9.5%,respectively.This study highlights the importance of more realistic and high-resolution forcing data in simulating vegetation growth and carbon exchange between the atmosphere and biosphere over the TP.展开更多
Agricultural applications of remote sensing data typically require high spatial resolution and frequent observations.The increasing availability of high spatial resolution imagery meets the spatial resolution requirem...Agricultural applications of remote sensing data typically require high spatial resolution and frequent observations.The increasing availability of high spatial resolution imagery meets the spatial resolution requirement well.However,the long revisit period and frequent cloud contamination severely compromise their ability to monitor crop growth,which is characterized by high temporal heterogeneity.Many spatiotemporal fusion methods have been developed to produce synthetic images with high spatial and temporal resolutions.However,these existing methods focus on fusing low and medium spatial resolution satellite data in terms of model development and validation.When it comes to fusing medium and high spatial resolution images,the applicability remains unknown and may face various challenges.To address this issue,we propose a novel spatiotemporal fusion method,the dual-stream spatiotemporal decoupling fusion architecture model,to fully realize the prediction of high spatial resolution images.Compared with other fusion methods,the model has distinct advantages:(a)It maintains high fusion accuracy and good spatial detail by combining deep-learning-based super-resolution method and partial least squares regression model through edge and color-based weighting loss function;and(b)it demonstrates improved transferability over time by introducing image gradient maps and partial least squares regression model.We tested the StarFusion model at 3 experimental sites and compared it with 4 traditional methods:STARFM(spatial and temporal adaptive reflectance fusion),FSDAF(flexible spatiotemporal data fusion),Fit-FC(regression model fitting,spatial filtering,and residual compensation),FIRST(fusion incorporating spectral autocorrelation),and a deep learning base method-super-resolution generative adversarial network.In addition,we also investigated the possibility of our method to use multiple pairs of coarse and fine images in the training process.The results show that multiple pairs of images provide better overall performance but both of them are better than other comparison methods.Considering the difficulty in obtaining multiple cloud-free image pairs in practice,our method is recommended to provide high-quality Gaofen-1 data with improved temporal resolution in most cases since the performance degradation of single pair is not significant.展开更多
Planted forest expansion often encroaches upon natural forests,leading to numerous environmental and social problems and altering the carbon sequestration capacity.Mapping natural and planted forests accurately is piv...Planted forest expansion often encroaches upon natural forests,leading to numerous environmental and social problems and altering the carbon sequestration capacity.Mapping natural and planted forests accurately is pivotal for achieving carbon neutrality and combating climate change.However,global mapping of natural and planted forests at fine spatial resolution remains an unmet requirement,mainly due to the insufficient number of training samples often needed in land cover mapping methods.This study presents a novel approach for automatically generating training samples and for accurately mapping the global distribution of natural and planted forests at 30-m spatial resolution in 2021.More than 70 million training samples were generated based on the distinct disturbance frequency of planted and natural forests across the 30-m Landsat images from 1985 to 2021 derived using a well-established time-series change detection method.These training samples encompass diverse Landsat and auxiliary data features,including spectral,structural,textural,and topographic attributes.Subsequently,locally adaptive random forest classifiers were trained using these samples and achieved an overall accuracy of 85%when validated against independent visually interpreted reference data.Based on the produced map,the proportions of the natural and planted forests for all the continents and countries were consistent with the Global Forest Resources Assessment 2020 statistics,indicated by regression slopes of 1.0050 and 1.2432,respectively.The generated training samples can be employed to update the global map of natural and planted forests.The produced map is expected to enhance our comprehension of variations in carbon sequestration,biodiversity maintenance,climate change mitigation,and other factors between natural and planted forests.Data presented in this study is publicly available at https://doi.org/10.5281/zenodo.10701417.展开更多
Measuring chemical concentrations at the surface of implanted medical devices is important for elucidating the local biochemical environment,especially during implant infection.Although chemical indicator dyes enable ...Measuring chemical concentrations at the surface of implanted medical devices is important for elucidating the local biochemical environment,especially during implant infection.Although chemical indicator dyes enable chemical measurements in vitro,they are usually ineffective when measuring through tissue because the background obscures the dye signal and scattering dramatically reduces the spatial resolution.X-ray excited luminescent chemical imaging(XELCI)is a recent imaging modality which overcomes these limitations using a focused X-ray beam to excite a small spot of red light on scintillator-coated medical implants with well-defined location(because X-rays are minimally scattered)and low background.A spectrochemical indicator film placed over the scintillator layer,e.g.,a polymer film containing pH-indicator dyes,absorbs some of the luminescence according to the local chemical environment,and this absorption is then detected by measuring the light intensity/spectrum passing through the tissue.A focused X-ray beam is used to scan point-by-point with a spatial resolution mainly limited by the X-ray beam width with minimum increase from X-ray absorption and scattering in the tissue.X-ray resolution,implant surface specificity,and chemical sensitivity are the three key features of XELCI.Here,we study spatial resolution using optically absorptive targets.For imaging a series of lines,the 20-80%knife-edge resolution was∼285(±15)μm with no tissue and 475±18 and 520±34μm,respectively,through 5 and 10 mm thick tissue.Thus,doubling the tissue depth did not appreciably change the spatial resolution recorded through the tissue.This shows the promise of XELCI for submillimeter chemical imaging through tissue.展开更多
In February 2025,a startup satellite manufacturer,Albedo(Broomfield,CO,USA)is expected to launch its first satellite,Clarity-1,into orbit aboard SpaceX’s Transporter-13,a Falcon 9 rideshare mission[1].Like many imagi...In February 2025,a startup satellite manufacturer,Albedo(Broomfield,CO,USA)is expected to launch its first satellite,Clarity-1,into orbit aboard SpaceX’s Transporter-13,a Falcon 9 rideshare mission[1].Like many imaging satellites,Clarity-1’s mis-sion will be to take high-resolution aerial photos for clients in var-ious economic sectors including agriculture,insurance,energy,mapping,utilities,and defense.What makes this satellite unique is both its industry-leading 10 cm spatial resolution and its extre-mely low orbit of 200 km,far closer to Earth than the 450 km or higher orbits of most of its peers with similar missions.展开更多
Gamma-ray imaging systems are powerful tools in radiographic diagnosis.However,the recorded images suffer from degradations such as noise,blurring,and downsampling,consequently failing to meet high-precision diagnosti...Gamma-ray imaging systems are powerful tools in radiographic diagnosis.However,the recorded images suffer from degradations such as noise,blurring,and downsampling,consequently failing to meet high-precision diagnostic requirements.In this paper,we propose a novel single-image super-resolution algorithm to enhance the spatial resolution of gamma-ray imaging systems.A mathematical model of the gamma-ray imaging system is established based on maximum a posteriori estimation.Within the plug-and-play framework,the half-quadratic splitting method is employed to decouple the data fidelit term and the regularization term.An image denoiser using convolutional neural networks is adopted as an implicit image prior,referred to as a deep denoiser prior,eliminating the need to explicitly design a regularization term.Furthermore,the impact of the image boundary condition on reconstruction results is considered,and a method for estimating image boundaries is introduced.The results show that the proposed algorithm can effectively addresses boundary artifacts.By increasing the pixel number of the reconstructed images,the proposed algorithm is capable of recovering more details.Notably,in both simulation and real experiments,the proposed algorithm is demonstrated to achieve subpixel resolution,surpassing the Nyquist sampling limit determined by the camera pixel size.展开更多
基金Project (2013CB227904) supported by the National Basic Research Program of ChinaProject (2012QNB09) supported by the Fundamental Research Funds for the Central University,ChinaProject (NCET-12-0956) supported by the Program for New Century Excellent Talents
文摘A surface soil moisture model with improved spatial resolution was developed using remotely sensed apparent thermal inertia（ATI）.The model integrates the surface temperature derived from TM/ETM＋ image and the mean surface temperature from MODIS images to improve the spatial resolution of soil temperature difference based on the heat conduction equation,which is necessary to calculate the ATI.Consequently,the spatial resolution of ATI and SMC can be enhanced from 1 km to 120 m（TM） or 60m（ETM＋）.Moreover,the enhanced ATI has a much stronger correlation coefficient（R^2） with SMC（0.789） than the surface reflectance（0.108） or the ATI derived only from MODIS images（0.264）.Based on the regression statistics of the field SMC measurement and enhanced ATI,a linear regression model with an RMS error of 1.90%was found.
基金supported by the National Basic Research Program (973) of China (No. 2008CB418104)the Major Programs of the Chinese Academy of Sciences (No. KZCX1-YW-14-4-1)the National Natural Science Foundation of China (No. 40901265)
文摘Impervious surfaces are the result of urbanization that can be explicitly quantified, managed and controlled at each stage of land development. It is a very useful environmental indicator that can be used to measure the impacts of urbanization on surface runoff, water quality, air quality, biodiversity and rnicroclimate. Therefore, accurate estimation of impervious surfaces is critical for urban environmental monitoring, land management, decision-making and urban planning. Many approaches have been developed to estimate surface imperviousness, using remotely sensed data with various spatial resolutions. However, few studies, have investigated the effects of spatial resolution on estimating surface imperviousness. We compare medium-resolution Landsat data with high-resolution SPOT images to quantify the imperviousness in Beijing, China. The results indicated that the overall 91% accuracy of estimates of imperviousness based on TM data was considerably higher than the 81% accuracy of the SPOT data. The higher resolution SPOT data did not always predict the imperviousness of the land better than the TM data. At the whole city level, the TM data better predicts the percentage cover of impervious surfaces. At the sub-city level, however, the ring belts from the central core to the urban-rural peripheral, the SPOT data may better predict the imperviousness. These results highlighted the need to combine multiple resolution data to quantify the percentage of imperviousness, as higher resolution data do not necessarily lead to more accurate estimates. The methodology and results in this study can be utilized to identify the most suitable remote sensing data to quickly and efficiently extract the pattern of the impervious land, which could provide the base for further study on many related urban environmental problems.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11435015 and 11505251)the Ministry of Science and Technology of China(Grant No.2016YFE0104900)the Chinese Academy of Sciences(Grant Nos.28Y740010 and 113462KYSB20160036)
文摘Ultrafast imaging tools are of great importance for determining the dynamic density distribution in high energy density(HED)matter.In this work,we designed a high energy electron radiography(HEER)system based on a linear electron accelerator to evaluate its capability for imaging HED matter.40 MeV electron beams were used to image an aluminum target to study the density resolution and spatial resolution of HEER.The results demonstrate a spatial resolution of tens of micrometers.The interaction of the beams with the target and the beam transport of the transmitted electrons are further simulated with EGS5 and PARMELA codes,with the results showing good agreement with the experimental resolution.Furthermore,the experiment can be improved by adding an aperture at the Fourier plane.
基金the National Natural Science Foundation of China(No.32273185)the National Key R&D Program of China(No.2020YFD0901205)the Marine Fishery Resources Investigation and Exploration Program of the Ministry of Agriculture and Rural Affairs of China in 2021(No.D-8006-21-0215)。
文摘Albacore tuna(Thunnus alalunga)is one of the target species of tuna longline fishing,and waters near the Cook Islands are a vital albacore tuna fishing ground.Marine environmental data are usually presented with different spatial resolutions,which leads to different results in tuna fishery prediction.Study on the impact of different spatial resolutions on the prediction accuracy of albacore tuna fishery to select the best spatial resolution can contribute to better management of albacore tuna resources.The nominal catch per unit effort(CPUE)of albacore tuna is calculated according to vessel monitor system(VMS)data collected from Chinese distantwater fishery enterprises from January 1,2017 to May 31,2021.A total of 26 spatiotemporal and environmental factors,including temperature,salinity,dissolved oxygen of 0–300 m water layer,chlorophyll-a concentration in the sea surface,sea surface height,month,longitude,and latitude,were selected as variables.The temporal resolution of the variables was daily and the spatial resolutions were set to be 0.5°×0.5°,1°×1°,2°×2°,and 5°×5°.The relationship between the nominal CPUE and each individual factor was analyzed to remove the factors irrelavant to the nominal CPUE,together with a multicollinearity diagnosis on the factors to remove factors highly related to the other factors within the four spatial resolutions.The relationship models between CPUE and spatiotemporal and environmental factors by four spatial resolutions were established based on the long short-term memory(LSTM)neural network model.The mean absolute error(MAE)and root mean square error(RMSE)were used to analyze the fitness and accuracy of the models,and to determine the effects of different spatial resolutions on the prediction accuracy of the albacore tuna fishing ground.The results show the resolution of 1°×1°can lead to the best prediction accuracy,with the MAE and RMSE being 0.0268 and 0.0452 respectively,followed by 0.5°×0.5°,2°×2°and 5°×5°with declining prediction accuracy.The results suggested that 1)albacore tuna fishing ground can be predicted by LSTM;2)the VMS records the data in detail and can be used scientifically to calculate the CPUE;3)correlation analysis,and multicollinearity diagnosis are necessary to improve the prediction accuracy of the model;4)the spatial resolution should be 1°×1°in the forecast of albacore tuna fishing ground in waters near the Cook Islands.
基金supported by the National Natural Science Foundation of China(31670552)the PAPD(Priority Academic Program Development)of Jiangsu provincial universities and the China Postdoctoral Science Foundation funded projectthis work was performed while the corresponding author acted as an awardee of the 2017 Qinglan Project sponsored by Jiangsu Province。
文摘Accurate information on the location and magnitude of vegetation change in scenic areas can guide the configuration of tourism facilities and the formulation of vegetation protection measures.High spatial resolution remote sensing images can be used to detect subtle vegetation changes.The major objective of this study was to map and quantify forest vegetation changes in a national scenic location,the Purple Mountains of Nanjing,China,using multi-temporal cross-sensor high spatial resolution satellite images to identify the main drivers of the vegetation changes and provide a reference for sustainable management.We used Quickbird images acquired in 2004,IKONOS images acquired in 2009,and WorldView2 images acquired in 2015.Four pixel-based direct change detection methods including the normalized difference vegetation index difference method,multi-index integrated change analysis(MIICA),principal component analysis,and spectral gradient difference analysis were compared in terms of their change detection performances.Subsequently,the best pixel-based detection method in conjunction with object-oriented image analysis was used to extract subtle forest vegetation changes.An accuracy assessment using the stratified random sampling points was conducted to evaluate the performance of the change detection results.The results showed that the MIICA method was the best pixel-based change detection method.And the object-oriented MIICA with an overall accuracy of 0.907 and a kappa coefficient of 0.846 was superior to the pixel-based MIICA.From 2004 to 2009,areas of vegetation gain mainly occurred around the periphery of the study area,while areas of vegetation loss were observed in the interior and along the boundary of the study area due to construction activities,which contributed to 79%of the total area of vegetation loss.During 2009–2015,the greening initiatives around the construction areas increased the forest vegetation coverage,accounting for 84%of the total area of vegetation gain.In spite of this,vegetation loss occurred in the interior of the Purple Mountains due to infrastructure development that caused conversion from vegetation to impervious areas.We recommend that:(1)a local multi-agency team inspect and assess law enforcement regarding natural resource utilization;and(2)strengthen environmental awareness education.
基金supported by the Sichuan Science and Technology Program,China(No.2020ZDZX0004)。
文摘Spatial resolution and image-processing methods for full-field X-ray fluorescence(FF-XRF)imaging using X-ray pinhole cameras were studied using Geant4simulations with different geometries and algorithms for image reconstruction.The main objectives were:(1)calculating the quantum efficiency curves of specific cameras,(2)studying the relationships between the spatial resolution and the pinhole diameter,magnification,and camera binning value,and(3)comparing image-processing methods for pinhole camera systems.Several results were obtained using a point and plane source as the X-ray fluorescence emitter and an array of 100×100 silicon pixel detectors as the X-ray camera.The quantum efficiency of a back-illuminated deep depletion(BI-DD)structure was above 30%for the XRF energies in the 0.8–9 keV range,with the maximum of 93.7%at 4 keV.The best spatial resolution of the pinhole camera was 24.7μm and 31.3 lp/mm when measured using the profile function of the point source,with the diameter of 20μm,magnification of 3.16,and camera bin of 1.A blind deconvolution algorithm with Gaussian filtering performed better than the Wiener filter and Richardson iterative methods on FF-XRF images,with the signal-to-noise ratio of 7.81 dB and improved signalto-noise ratio of 7.24 dB at the diameter of 120μm,magnification of 1.0,and camera bin of 1.
文摘Remote sensing has played a pivotal role in our understanding of the geometry of dykes and dyke swarms on Earth,Venus and Mars(West and Ernst,1991;Mege and Masson,1995;Ernst et al.,2005).Since the 1970’s
基金The National Key Technology Research and Development Program of China under contract No.2012BAB16B01
文摘A spatial resolution effect of remote sensing bathymetry is an important scientific problem. The in situ measured water depth data and images of Dongdao Island are used to study the effect of water depth inversion from different spatial resolution remote sensing images. The research experiments are divided into five groups including Quick Bird and World View-2 remote sensing images with their original spatial resolution（2.4/2.0 m）and four kinds of reducing spatial resolution（4, 8, 16 and 32 m）, and the water depth control and checking points are set up to carry out remote sensing water depth inversion. The experiment results indicate that the accuracy of the water depth remote sensing inversion increases first as the spatial resolution decreases from 2.4/2.0 to 4, 8 and16 m. And then the accuracy decreases along with the decreasing spatial resolution. When the spatial resolution of the image is 16 m, the inversion error is minimum. In this case, when the spatial resolution of the remote sensing image is 16 m, the mean relative errors（MRE） of Quick Bird and World View-2 bathymetry are 21.2% and 13.1%,compared with the maximum error are decreased by 14.7% and 2.9% respectively; the mean absolute errors（MAE） are 2.0 and 1.4 m, compared with the maximum are decreased by 1.0 and 0.5 m respectively. The results provide an important reference for the selection of remote sensing data in the study and application of the remote sensing bathymetry.
基金Supported by the Key Science and Technology Projects of Guizhou Province,China[(2007)3017,(2008)3022]Major Special Project of Guizhou Province,China(2006-6006-2)
文摘[ Objective] The study aimed to improve methods of monitoring Karst Rocky Desertification （KRD） control projects and increase the working efficiency. [Method] Based on remote sensing images with medium and high spatial resolution, KRD control projects in Disi River basin in Puan County were monitored, that is, information of the project construction in the study area was extracted using supervised classification and hu- man-computer interactive interpretation, and the monitoring results were testified with the aid of GPS. [Result] It was feasible to monitor KRD con- trol projects in Disi River basin based on remote sensing images with medium and high resolution, and the monitoring accuracy was satisfactory, reaching above 80% or 90%, so the method is worthy of popularizing. [ Conclusion] Remote sensing images with medium and high resolution can be used to monitor other KRD control Droiects.
基金Under the auspices of National Key Research and Development Project of China(No.2021YFD1500103)Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA28100500)+2 种基金National Natural Science Foundation of China(No.4197132)Science and Technology Development Plan Project of Jilin Province(No.20210201044GX)Land Observation Satellite Supporting Platform of National Civil Space Infrastructure Project(No.CASPLOS-CCSI)。
文摘Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing data processing is time-consuming and resource-intensive,and significantly hampers the efficiency and timeliness of soil moisture mapping.Due to the high-speed computing capabilities of remote sensing cloud platforms,a High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF)based on the Google Earth Engine(GEE)platform was developed in this study.The functions of the HSRSMEF include research area and input datasets customization,radar speckle noise filtering,optical-radar image spatio-temporal matching,soil moisture retrieving,soil moisture visualization and exporting.This paper tested the performance of HSRSMEF by combining Sentinel-1,Sentinel-2 images and insitu soil moisture data in the central farmland area of Jilin Province,China.Reconstructed Normalized Difference Vegetation Index(NDVI)based on the Savitzky-Golay algorithm conforms to the crop growth cycle,and its correlation with the original NDVI is about 0.99(P<0.001).The soil moisture accuracy of the random forest model(R 2=0.942,RMSE=0.013 m3/m3)is better than that of the water cloud model(R 2=0.334,RMSE=0.091 m3/m3).HSRSMEF transfers time-consuming offline operations to cloud computing platforms,achieving rapid and simplified high spatial resolution soil moisture mapping.
基金supported by the National Key R&D Program of China(Grant No.2017YFA0403702)the National Natural Science Foundation of China(Grant Nos.11574123,11775243,12175254,and U2032166)+1 种基金Youth Innovation Promotion Association CAS and Guangdong Basic and Applied Basic Research Foundation(Grant No.2019A1515110217)the Xie Jialin Foundation,China(Grant No.E1546FU2)。
文摘In recent years,gas electron multiplier(GEM)neutron detectors have been developing towards high spatial resolution and high dynamic counting range.We propose a novel concept of an Al stopping layer to enable the detector to achieve sub-millimeter(sub-mm)spatial resolution.The neutron conversion layer is coated with the Al stopping layer to limit the emission angle of ions into the drift region.The short track projection of ions is obtained on the signal readout board,and the detector would get good spatial resolution.The spatial resolutions of the GEM neutron detector with the Al stopping layer are simulated and optimized based on Geant4 Garfield Interface.The spatial resolution of the detector is 0.76 mm and the thermal neutron detection efficiency is about 0.01%when the Al stopping layer is 3.0μm thick,the drift region is 2 mm thick,the strip pitch is 600μm,and the digital readout is employed.Thus,the GEM neutron detector with a simple detector structure and a fast readout mode is developed to obtain a high spatial resolution and high dynamic counting range.It could be used for the direct measurement of a high-flux neutron beam,such as Bragg transmission imaging,very small-angle scattering neutron detection and neutron beam diagnostic.
基金financial support during the development of this study(Process#:E-26/170.407/2000)and MCTI/Observatorio Nacional for all support.
文摘A novel methodology to quantify the spatial resolution in 2-D seismic surface wave tomographic problems is proposed in this study. It is based on both the resolving kernels computed via full resolution matrix and the concept of Full Width at Half Maximum (FWHM) of a Gaussian function. This method allows estimating quantitatively the spatial resolution at any cell of a gridded area. It was applied in the northeastern Brazil and the estimated spatial resolution range is in agreement with all previous surface wave investigations in the South America continent.
基金supported by the Natural Science Research General Program of Shanxi Province Basic Research Project(Grant No.202203021221231).
文摘Land use and land cover(LULC)has undergone drastic changes with the rapid growth of the global population,economic development,and the expansion of agricultural activities.However,the uncertainty of classification algorithms and image resolution based on satellite data for land cover mapping,particularly cropland cover mapping,needs to be investigated sufficiently.In this study,the influence of different spatial-resolution images on classification results was explored by comparing the differences between four machine learning algorithms for LULC mapping.The classification results of this model were also compared with existing global land cover datasets to determine whether the model was capable of producing reliable results.According to the results of this study,the random forest(RF)classifier outperformed the support vector machine(SVM),decision tree(DT),and artificial neural network(ANN)with an overall accuracy(OA)and kappa coefficient of 81.99%and 0.78,respectively.However,SVM and ANN showed greater accuracy on the water class and unused land class,respectively.With increasing spatial resolution,RF’s accuracy increased initially and then decreased when classifying images with five different spatial resolutions(30 m,16 m,10 m,8 m,and 2 m).In particular,with an OA of 82.54%and a kappa coefficient of 0.78,RF performed the best on images with 8 m resolution.Additionally,the RF-based image with 8 m resolution produced a higher OA of 0.88 for cropland.Topography is the main factor that determines the classification performance of different-resolution images.The classification accuracies of RF10 m and RF30 m(10 m and 30 m resolution images,respectively,using RF)were higher(OAs of 93.59%and 94.59%,respectively)than those of the global land cover dataset(LC10 m and LC30 m,land cover images with 10 m and 30 m resolution,respectively),whose high-resolution images showed more details of the land cover.The results of this study highlight that classification algorithms and image resolution are the sources of uncertainty for land mapping.Obtaining reliable land cover mapping requires the use of appropriate classification algorithms and spatial resolution.With these results,it will be possible to develop a national land monitoring system and basic ecological climate models using LULC.
基金supported by the National Key Research and Development Program of China(2021YFA1302500)the National Natural Science Foundation of China(32030022,32325012,31970642)the Science and Technology Commission of Shanghai Municipality(23JS1401200).
文摘The resolution of most spatially resolved transcriptomic technologies usually cannot attain the single-cell level,limiting their applications in biological discoveries.Here,we introduce ImSpiRE,an image feature-aided spatial resolution enhancement method for in situ capturing spatial transcriptome.Taking the information stored in histological images,ImSpiRE solves an optimal transport problem to redistribute the expression profiles of spots to construct new transcriptional profiles with enhanced resolution,together with extending the gene expression profiles into unmeasured regions.Applications to multiple datasets confirm that ImSpiRE can enhance spatial resolution to the subspot level while contributing to the discovery of tissue domains,signaling communication patterns,and spatiotemporal characterization.
基金the National Natural Science Foundation of China(Grant Nos.42130602,42175136)the Collaborative Innovation Center for Climate Change,Jiangsu Province,China.
文摘The efficacy of vegetation dynamics simulations in offline land surface models(LSMs)largely depends on the quality and spatial resolution of meteorological forcing data.In this study,the Princeton Global Meteorological Forcing Data(PMFD)and the high spatial resolution and upscaled China Meteorological Forcing Data(CMFD)were used to drive the Simplified Simple Biosphere model version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics(SSiB4/TRIFFID)and investigate how meteorological forcing datasets with different spatial resolutions affect simulations over the Tibetan Plateau(TP),a region with complex topography and sparse observations.By comparing the monthly Leaf Area Index(LAI)and Gross Primary Production(GPP)against observations,we found that SSiB4/TRIFFID driven by upscaled CMFD improved the performance in simulating the spatial distributions of LAI and GPP over the TP,reducing RMSEs by 24.3%and 20.5%,respectively.The multi-year averaged GPP decreased from 364.68 gC m^(-2)yr^(-1)to 241.21 gC m^(-2)yr^(-1)with the percentage bias dropping from 50.2%to-1.7%.When using the high spatial resolution CMFD,the RMSEs of the spatial distributions of LAI and GPP simulations were further reduced by 7.5%and 9.5%,respectively.This study highlights the importance of more realistic and high-resolution forcing data in simulating vegetation growth and carbon exchange between the atmosphere and biosphere over the TP.
基金supported by High-Resolution Earth Observation System(09-Y30F01-9001-20/22).
文摘Agricultural applications of remote sensing data typically require high spatial resolution and frequent observations.The increasing availability of high spatial resolution imagery meets the spatial resolution requirement well.However,the long revisit period and frequent cloud contamination severely compromise their ability to monitor crop growth,which is characterized by high temporal heterogeneity.Many spatiotemporal fusion methods have been developed to produce synthetic images with high spatial and temporal resolutions.However,these existing methods focus on fusing low and medium spatial resolution satellite data in terms of model development and validation.When it comes to fusing medium and high spatial resolution images,the applicability remains unknown and may face various challenges.To address this issue,we propose a novel spatiotemporal fusion method,the dual-stream spatiotemporal decoupling fusion architecture model,to fully realize the prediction of high spatial resolution images.Compared with other fusion methods,the model has distinct advantages:(a)It maintains high fusion accuracy and good spatial detail by combining deep-learning-based super-resolution method and partial least squares regression model through edge and color-based weighting loss function;and(b)it demonstrates improved transferability over time by introducing image gradient maps and partial least squares regression model.We tested the StarFusion model at 3 experimental sites and compared it with 4 traditional methods:STARFM(spatial and temporal adaptive reflectance fusion),FSDAF(flexible spatiotemporal data fusion),Fit-FC(regression model fitting,spatial filtering,and residual compensation),FIRST(fusion incorporating spectral autocorrelation),and a deep learning base method-super-resolution generative adversarial network.In addition,we also investigated the possibility of our method to use multiple pairs of coarse and fine images in the training process.The results show that multiple pairs of images provide better overall performance but both of them are better than other comparison methods.Considering the difficulty in obtaining multiple cloud-free image pairs in practice,our method is recommended to provide high-quality Gaofen-1 data with improved temporal resolution in most cases since the performance degradation of single pair is not significant.
基金supported by the National Natural Science Foundation of China under Grants 42222108 and 42171345.
文摘Planted forest expansion often encroaches upon natural forests,leading to numerous environmental and social problems and altering the carbon sequestration capacity.Mapping natural and planted forests accurately is pivotal for achieving carbon neutrality and combating climate change.However,global mapping of natural and planted forests at fine spatial resolution remains an unmet requirement,mainly due to the insufficient number of training samples often needed in land cover mapping methods.This study presents a novel approach for automatically generating training samples and for accurately mapping the global distribution of natural and planted forests at 30-m spatial resolution in 2021.More than 70 million training samples were generated based on the distinct disturbance frequency of planted and natural forests across the 30-m Landsat images from 1985 to 2021 derived using a well-established time-series change detection method.These training samples encompass diverse Landsat and auxiliary data features,including spectral,structural,textural,and topographic attributes.Subsequently,locally adaptive random forest classifiers were trained using these samples and achieved an overall accuracy of 85%when validated against independent visually interpreted reference data.Based on the produced map,the proportions of the natural and planted forests for all the continents and countries were consistent with the Global Forest Resources Assessment 2020 statistics,indicated by regression slopes of 1.0050 and 1.2432,respectively.The generated training samples can be employed to update the global map of natural and planted forests.The produced map is expected to enhance our comprehension of variations in carbon sequestration,biodiversity maintenance,climate change mitigation,and other factors between natural and planted forests.Data presented in this study is publicly available at https://doi.org/10.5281/zenodo.10701417.
基金funded by the NIH NIAMS R01 AR070305-01.U.U.was supported by a NIH R01 grant and a Fulbright Scholar Award.
文摘Measuring chemical concentrations at the surface of implanted medical devices is important for elucidating the local biochemical environment,especially during implant infection.Although chemical indicator dyes enable chemical measurements in vitro,they are usually ineffective when measuring through tissue because the background obscures the dye signal and scattering dramatically reduces the spatial resolution.X-ray excited luminescent chemical imaging(XELCI)is a recent imaging modality which overcomes these limitations using a focused X-ray beam to excite a small spot of red light on scintillator-coated medical implants with well-defined location(because X-rays are minimally scattered)and low background.A spectrochemical indicator film placed over the scintillator layer,e.g.,a polymer film containing pH-indicator dyes,absorbs some of the luminescence according to the local chemical environment,and this absorption is then detected by measuring the light intensity/spectrum passing through the tissue.A focused X-ray beam is used to scan point-by-point with a spatial resolution mainly limited by the X-ray beam width with minimum increase from X-ray absorption and scattering in the tissue.X-ray resolution,implant surface specificity,and chemical sensitivity are the three key features of XELCI.Here,we study spatial resolution using optically absorptive targets.For imaging a series of lines,the 20-80%knife-edge resolution was∼285(±15)μm with no tissue and 475±18 and 520±34μm,respectively,through 5 and 10 mm thick tissue.Thus,doubling the tissue depth did not appreciably change the spatial resolution recorded through the tissue.This shows the promise of XELCI for submillimeter chemical imaging through tissue.
文摘In February 2025,a startup satellite manufacturer,Albedo(Broomfield,CO,USA)is expected to launch its first satellite,Clarity-1,into orbit aboard SpaceX’s Transporter-13,a Falcon 9 rideshare mission[1].Like many imaging satellites,Clarity-1’s mis-sion will be to take high-resolution aerial photos for clients in var-ious economic sectors including agriculture,insurance,energy,mapping,utilities,and defense.What makes this satellite unique is both its industry-leading 10 cm spatial resolution and its extre-mely low orbit of 200 km,far closer to Earth than the 450 km or higher orbits of most of its peers with similar missions.
基金supported by the National Natural Science Foundation of China(Grant No.12175183)。
文摘Gamma-ray imaging systems are powerful tools in radiographic diagnosis.However,the recorded images suffer from degradations such as noise,blurring,and downsampling,consequently failing to meet high-precision diagnostic requirements.In this paper,we propose a novel single-image super-resolution algorithm to enhance the spatial resolution of gamma-ray imaging systems.A mathematical model of the gamma-ray imaging system is established based on maximum a posteriori estimation.Within the plug-and-play framework,the half-quadratic splitting method is employed to decouple the data fidelit term and the regularization term.An image denoiser using convolutional neural networks is adopted as an implicit image prior,referred to as a deep denoiser prior,eliminating the need to explicitly design a regularization term.Furthermore,the impact of the image boundary condition on reconstruction results is considered,and a method for estimating image boundaries is introduced.The results show that the proposed algorithm can effectively addresses boundary artifacts.By increasing the pixel number of the reconstructed images,the proposed algorithm is capable of recovering more details.Notably,in both simulation and real experiments,the proposed algorithm is demonstrated to achieve subpixel resolution,surpassing the Nyquist sampling limit determined by the camera pixel size.
