最新刊期
卷 20 ， 期 1 ， 2016
- 摘要：This study reviews the historical development process of spaceborne Li DAR for atmospheric detection along with the major achievements to date. The future plan with regard to this technology is investigated as well. First,we review the history of several successfully launched spaceborne Li DARs,beginning with the first spaceborne Li DAR technology experiment LITE. Then,we expound on the primary achievements in the field of atmospheric remote sensing,particularly in terms of climatic and environmental changes,and highlight numerical prediction model studies based on spaceborne Li DARs,such as LITE and CALIOP. Existing achievements include（ 1） the global aerosol vertical structure and its attendant radiative forcing,（ 2） the global cloud vertical structure and its associated features,（ 3） aerosol-cloud-precipitation interaction,and（ 4） the applications of aerosol vertical data in the numerical prediction of fog / haze and dust. We intensively analyze future demands and challenges in terms of atmospheric wind field detection and atmospheric constituent retrieval from a spaceborne Li DAR. With respect to wind field retrieval,a spaceborne Li DAR may enhance the accuracy of weather forecasts in tropical regions,improve the nowcasting of small-scale and mesoscale weather under the nongeostrophic condition,and fill in the gaps with regard to monitoring an upper / lower level jet stream.Li DARs also have competitive advantages over traditional passive sensors with regard to atmospheric constituent retrieval because of the former’s high signal-to-noise ratio,unique CO2 vertical structure,and nighttime detection capability. Finally,the spaceborne Li DAR may be used to measure global wind field and atmospheric constituents.关键词：spaceborne LiD AR;atmospheric detection;wind measurement;aerosol;cloud
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发布时间：2021-06-10 - 摘要：Biomass burning is a widespread practice. During burning,fire combusts organic matter and emitsalarge amount of carbonaceous gases into the atmosphere. Biomass burning not only changes the structure and process of the ecosystem but also affects the carbon cycle of the entire system. To elucidate the impact of wildfire on global carbon cycle,large-scale carbon emissions from biomass burning have been estimated using satellite remote sensing. Many remote-sensing-based models have been developed to estimate biomass burning emissions at different scales. The most widely used model contains four key parameters: burned area,fuel load,burning efficiency,and carbon fraction. The first three parameters can be retrieved from satellite data. This paper discussesmethodologies for the retrieval of these three key input parameters anddescribesthe advantages and disadvantages of each methodology. Methods for the estimation of burned area can be categorized into three types: reflectance-,emission-,and backscatter feature-based methods. Fuel load mapping can be classified as direct and indirect. Indirect fuel load mappingclassifies satellite data to determine the fuel typeand then assigns fuel load value to each pixel depending on the fuel type in the fuel models. This method strongly relies on fuel model and ismostly not suitable for large-scale areas. Direct fuel load mapping estimates fuel load value on the basis of the relationship among fuel load,relative factor of fuel load,and satellite data. Burning efficiency or combustion completeness is usually estimated through direct and indirect retrievalmethods. The direct retrievalmethod is difficult to be usedat alarge scale,whereasthe indirect retrievalmethod maps the burn severity firstand then adjusts the preset fixed burning efficiency on the basis ofburn severity. Finally,suggestions are provided to improve the accuracy of remote sensing in estimating carbon emissions from biomass burning. Many studies have been conducted to retrieve carbon emission-related parameters throughremote sensing. However,the adaptability and uncertainty of theseestimationsfor large-scale areas remain unclear,andthe estimationaccuracy ofglobal carbon emission does not satisfy the demand of research on carbon cycle.关键词：biomass burning;carbon emission;burned area;fuel loading;burning efficiency
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发布时间：2021-06-10 - 摘要：The key to extracting heavy metal from water through remote sensing is to accurately obtain the absorption coefficient spectrum of the dissolved heavy metal. The first step is to calculate the absorption coefficient per unit concentration of each dissolved heavy metal. The absorption coefficient is an important parameter in our remote sensing inversion model of heavy metals in water. We measure copper ions,which are common heavy metals in water that display evident characteristics in the absorption spectrum. Therefore,extracting copper ions by using remote sensing is a breakthrough. We designed equipment,which can adjust the path length of passing light and provide accurate results at the visible and near-infrared wavelength range. Then,we use an Analytical Spectral Devices（ ASD） spectrometer to measure the radiance of direct light passing through copper ion solutions with different concentrations on the standard board. Using the ratio method to eliminate environmental errors and the effect of suspended solids in water,we calculate the extinction coefficient and the absorption coefficient per g / L of copper ion solutions. Finally,we obtain the absorption spectrum per g / L of copper ions from 400 nm to 900 nm. The absorption coefficient in the blue and green solutions is very low but rapidly increases from the red to near-infrared region,which coincides with the color of copper ions. Maximum absorption is observed at 810 nm,and the absorption coefficient is larger in red than in blue and green. This observation is caused by the d-d transition in the outermost electrons of copper ions,which mostly absorb the energy of red color. We perform numerous independent experiments and find that the standard deviations of the results are minimal,indicating the stability of our measurement results. Our results are consistent and more reasonable and accurate than those of Jancsò’s in some wavelength ranges. This conclusion is based on observations of the different light colors on the standard board between pure water and high concentration copper ion solutions. We use the absorption coefficient at 722 nm to calculate the concentration of some copper ion solutions and compare them with the real concentrations. The relative errors are less than 5%,which verifies the accuracy of our results at 722 nm. We conclude that the obtained absorption spectrum is reasonable and accurate. The results can be used as the base parameter in the remote sensing inversion model of copper ions in water. Our results suggest that the maximum absorption is at810 nm,indicating that this wavelength is the most sensitive to copper ion concentration changes in water.关键词：heavy metal pollution in water;copper ion;absorption spectrum;measuring equipment;water quality remote sensing
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发布时间：2021-06-10 - 摘要：The key to extract the contents of ferric ion in water through the remote sensing technique is to measure the absorption spectrums of the dissolved ferric compounds. This paper selects three types of ferric compounds（ i. e.,ferric chloride,ferric sulfate,and potassium ferricyanide）,which are most common in nature,to measure their absorption spectrums. The equipment is designed to adjust the path length of passing light and increase the accuracy of the results at visible and near-infrared wavelength range. An Analytical Spectral Devices（ ASD） spectrometer measures the radiance of direct light passing through ferric ion solutions of different concentrations on a standard board. The ratio method is used to eliminate environmental errors and the effect of the suspended solids in water. The extinction coefficient and the absorption coefficient per mg / L of three types of ferric ion solutions are calculated. Finally,the absorption coefficient per mg / L of the three types of ferric ion from 400 nm to 900 nm is obtained. Results indicate that all three types of absorption coefficient in blue are larger than green and red and slightly change from red to near-infrared. Moreover,the value of the absorption coefficient of three types of ferric ion from large to small（ i. e.,potassium ferricyanide,ferric sulfate,and ferric chloride） ranges from 400 nm to 455 nm. Furthermore,the absorption coefficient of ferric sulfate is larger than potassium ferricyanide and ferric chloride and ranges from 460 nm to 900 nm. The characteristic of absorption spectrum of the types of ferric ion exhibits a slight difference because of the different outermost electronic structure. The wavelength,which has the maximum of average relative error,is used in the absorption coefficient（ i. e.,ferric chloride at 417 nm,potassium ferricyanide at469 nm,and ferric sulfate at 624 nm） to calculate the concentration of its ferric ion solutions and compare them with the real concentrations. The relative errors are less than 5%,which verifies that results are accurate. The measured absorption spectrum is reasonable and accurate. Results can be used as the base parameter in the remote sensing inversion model of ferric ion in water.The largest absorption coefficient appears in blue,which suggests blue is the most sensitive wavelength to detect the change of ferric ion concentration in water.关键词：absorption coefficient spectrum;complex;ferric ion;heavy metal pollution in water;hydrated;measuring;water quality remote sensing
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发布时间：2021-06-10 - 摘要：The amount of hydrometeors in clouds plays an important role in the Earth’s radiation balance. It is also an important parameter in representing clouds in global circulation models used for climate study and weather forecasting. Satellite data have been widely used to estimate global atmospheric parameters. In this study,we introduce in detail a 1D-Var retrieval algorithm andassess the quality of the devived hydrometeor products. This algorithm could provide an estimate of the geophysical state,especially hydrometeor profiles,which are used as first guess and / or background before starting data assimilation. This algorithm is beneficial to the assimilation of satellite measurements under cloudy and rainy conditions.A one-dimensional variational retrieval algorithm is developed to retrieve hydrometeor parameters（ profiles of liquid cloud,liquid precipitation,and ice cloud） from spaceborne microwave AMSR-E /2 measurements. The algorithm is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. It inverts the radiative transfer equation by finding radiometrically appropriate profiles of geophysical parameters. In addition,the retrieved parameters include a set of derived products that are a simple vertical integration of fundamental profiles,such as total precipitable water,cloud liquid water,ice water path,and rainfall rate.AMSR-2 measurements from Halong Typhoon in 2014 were used as examples,and all of the retrieved products were assessed These hydrometeor profiles were integrated into the radiative transform model（ observation operators）,in which cloud absorption and scattering effect were measured. The simulated and observed brightness temperatures were consistent in all microwave channels. The retrieved hydrometeor profiles were validated using the observed reflectivities of the Cloud Profiling Radar uploaded on Cloud Sat satellite. Comparison results showed that areas with high radar reflectivity matched the cloud water content and liquid precipitation regions at high amounts,proving the high precision of hydrometeor retrievals from the 1D-Var algorithm. However,the AMSR-E /2 observations were not sensitive to small-scale shallow clouds because of its few channels and poor spatial resolution.In addition,the inversion ability of satellite microwave measurments was limited to overcast or layered clouds with a high optical thickness.These hydrometeor parameters are extremely difficult to assess because of the lack of effective ways to measure these quantities（ either from ground-based or satellite sensors）. Mutual validation of these hydrometeor products from different sensors for long periods of time is still needed.关键词：AMSR-E /2;1D-Var retrieval;hydrometeor profile;CloudSat;precision validation
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发布时间：2021-06-10 - 摘要：Mineral mixtures are intimate mixtures have been extensively generated both on Earth and on other planets. The scatter of the particles that comprise mineral mixtures typically initiates complex optical interactions that are not interpreted by a linear mixing model. Moreover,the extraction of endmember signatures from the original data may be difficult given complex mineral mixtures and the lack of completely pure spectral signatures in the scene. Thus,we propose a sparse unmixing algorithm based on the spectral library of a Single Scattering Albedo（ SSA） to decompose intimate mixtures.The sparse unmixing method aims to determine the optimal subset of endmembers from a spectral library and to estimate its fractional abundances in each pixel. The data from the spectral library are considered prior information; thus,sparse unmixing does not rely on endmember extraction algorithms. The Hapke model can describe intimate mixing effects effectively,and SSA can be obtained based on the theory behind this model. The SSA of the component particles is a linear mixture; therefore,linear unmixing techniques can be applied in the SSA space instead of in the reflectance space. The data processing procedure in this study mainly consists of three steps:（ 1） Building the spectral library of reflectance and then resampling the library spectrum to wavelength range and position;（ 2） Converting the reflectance to SSA and constructing a SSA spectral library;（ 3） Sparse unmixing.The laboratory spectra of the mineral mixtures and of the AVIRIS Cuprite data set are used to verify our method. We can identify the endmembers of mixtures accurately given laboratory data,and the mean absolute retrieval error of abundance is 3. 12%.The qualitative analysis of AVIRIS hyperspectral data indicates that the abundance maps derived with our method are consistent with the Tricorder maps of United States Geological Survey（ USGS）,particularly in places where abundance is high; nonetheless,the abundance maps determined with our method still do not confirm to Tricorder maps to some extent. The classification maps of hyperspectral data consider each pixel to be pure,and each pixel is classified as the class of the representative endmember in the pixel. By contrast,unmixing classifies the scene at subpixel level,and the abundances represent the proportion of endmembers in a pixel.Experiment results show that our method can identify endmembers from the spectral library accurately,and can also estimate abundance well. The Hapke model can simply be used to minimize errors by calculating SSA directly. In our future work,we will calculate SSA exactly based on physical mineral parameters.关键词：spectral unmixing;Hapke model;sparse regression;spectral library;single scattering albedo
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发布时间：2021-06-10 - 摘要：High spatial and temporal resolution remote sensing data play an important role in monitoring the rapidly changing information regarding the earth’s surface. However,the spatial and temporal resolutions of a remote sensing image for a specific sensor contain irreconcilable conflict. Fusing the spatial and temporal features of different remote sensing images to generate high-spatialtemporal remote sensing data is an effective method to solve the contradiction. This paper aims to improve the fusion data performance of STARFM in heterogeneous areas by downscaling rather than directly resampling a coarse pixel.The combination of the downscaling mixed pixel method and the spatial and temporal adaptive reflectance fusion model（ STARFM）（ CDSTARFM） was proposed. In this approach,the downscaling mixed pixel method first reduces the MODIS data that participated in the fusion. Then,the downscaled MODIS data replaces the direct resample MODIS data that appeared in the original STARFM. Finally,the subsequent steps of the STARFM are completed to predict Landsat-like images. The proposed algorithm produced high-resolution temporal synthetic Landsat 8 data based on Landsat 8 and MODIS remote sensing images.Results show that the CDSTARFM was more accurate than STARFM and downscaling methods. The downscaled data used in the CDSTARFM can more fairly reflect surface information than the resampled data of MODIS,which increase the probability of"pure pixels"and allow the CDSTARFM method to more easily determine the"pure"similar pixels in the search window. Therefore,the three indicators [i. e.,correlation coefficient（ r）,root mean square error（ RMSE）,and the Erreur Relative Globale Adimensionalle de Synthèse（ ERGAS） ] as well as the scatterplots and visual effect of synthetic images for the CDSTARFM are better than those obtained by STARFM and downscaling methods. Moreover,the optimal window size（ 11 × 11 OLI pixels） of the CDSTARFM is smaller than that of the STARFM（ 31 × 31 OLI pixels）. The accuracy of the CDSATRFM at the same window size is higher than that of the STARFM. The predicted NIR band is evaluated as an example in this study. The correlation coefficients（ r） for the CDSTARFM,STARFM,and downscaling methods were 0. 96,0. 95,0. 90,respectively; RMSEs were 0. 0245,0. 0300,0. 0401,respectively; and ERGAS were 0. 5416,0. 6507,0. 8737,respectively. Moreover,synthetic images effectively eliminated the"homogeneous spot"that appeared in the fusion images predicted by the downscaling algorithm and the"boundary of MODIS pixel"that appeared in the synthetic images produced by the STARFM.The temporal-spatial fusion of remote sensing data is an effective approach to solve the conflict of temporal and spatial resolutions of a sensor. This paper used the CDSTARFM algorithm,which combines downscaling method and STARFM to fuse remote sensing data. The CDSTARFM algorithm was verified by the experimental data of Landsat 8 and MODIS images,and it was compared with the STARFM and downscaling methods. The conclusions are listed bellow:（ 1） CDSTARFM using the downscaled data presented improved results to replace the directly resampled data used in STARFM. The three indicators（ i. e.,r,RMSE,and ERGAS） and the scatterplots for the CDSTARFM are the best compared with those for STARFM and downscaling methods.（ 2） The window size at which the best synthetic images were predicted by CDSTARFM is smaller than that by STARFM. In addition,CDSTARFM exhibits better accuracy than STARFM and downscaling methods at the same window size.（ 3） The synthetic images produced by CDSTARFM are more visually similar to the reference images,especially in the fragmented regions. CDSTARFM can eliminate the homogeneous"plot"in the synthetic images that were generated by downscaling methods and the "MODIS pixel boundary"in the prediction images generated by STARFM in fragmented landscapes.关键词：decomposition of mixed pixel;downscaling;STARFM;remote sensing;data fusion;CDSTARFM
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发布时间：2021-06-10 - 摘要：To improve the automatic target recognition accuracy of SAR images and real-time performance,this study proposes a feature selection algorithm based on hybrid intelligent optimization for such images. First,a fractal feature is used to enhance an SAR image. An azimuth estimation method is then developed based on the image moment after image segmentation. Subsequently,the features of Zernike moment,Gabor wavelet coefficients,and gray level co-occurrence matrix are extracted from the original and the rectified images to form feature candidates. The genetic algorithm and the binary particle swarm optimization algorithm are combined to select features for SAR images. The effectiveness of the proposed algorithm is verified with the MSTAR database. Results demonstrate that the optimal feature sets can be generalized,thereby improving the target recognition rate and reducing recognition time.关键词：SAR image;feature selection;hybrid intelligent optimization algorithm;fractal feature;Zernike moment
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发布时间：2021-06-10 - 摘要：This paper presents our research on registering single aerial image to a LiDAR point cloud. Given its high spatial resolution,spatial positioning accuracy,and efficiency in capturing data of physical surfaces,LiDAR has been influenced by and has significantly changed photogrammetry. The fusion of LiDAR data with aerial images offers various applications,such as DOM generation,virtual reality,city modeling,and military training,because of the complementary nature of the information provided by the two systems. However,the two datasets should be geo-registered into a common coordinate frame prior to such integration,which proves to be quite challenging in terms of either automation or accuracy. Such a challenge may be partly caused by inefficiency in the feature measurement or detection stage. For example,the identification of point of interest or straight line feature is viable and reliable in optical images but is difficult to achieve in LiDAR point clouds because of its poor discontinuity measurements. To this end,an automatic geo-registration approach based on "pin-hole "imaging simulation and iterative gradient mutual information computation is proposed to align single aerial image to discrete LiDAR point clouds. The proposed approach takes photogrammetry collinear equation as strict mathematic mode and involves three stages. First,a virtual "pin-hole"imaging process restored from aerial image orientation parameters is established on urban LiDAR point clouds to generate simulated,gray,LiDAR-depth images.The generated LiDAR-depth images are geometrically similar to aerial images. Hence,difficulties in registration caused by distinct differences in spatial resolution,perspective distortion,and size between the two types of data sources can be greatly alleviated.Second,the geometric transform parameters between LiDAR depth images and aerial images are successfully estimated with the gradient mutual information as the similarity measurement. Moreover,the image pyramid partitioning strategy is implemented to accelerate the search for parameter space. In this stage,LiDAR laser feet points can be roughly mapped on aerial image pixels on the basis of the estimated geometric transform parameters and the known projection relations between LiDAR point clouds and their depth images. Third,the photogrammetry space resection algorithm is implemented using all the mapped aerial image pixels as observed values and their gradient mutual information as weight to improve image orientation parameters. The three stages are repeated until the given iterative calculation condition is met and the LiDAR point clouds are registered with single aerial image.Selected airborne LiDAR data and an aerial image with different initial parameter values are tested with the proposed approach.Approximately 0. 5 pixel is obtained,indicating a higher registration precision compared with the ICP algorithm.（ 1） The "pinhole"simulation imaging and iterative gradient mutual information calculation successfully resolve the difficult heterologous correspondence problem between LiDAR point clouds and optical aerial images;（ 2） The photogrammetry space resection algorithm can obtain registration parameters with minimum projection errors and reliable precision evaluation by maximizing the use of intensive space information from LiDAR data and recovering optical bundles of laser beams directly.关键词：Image geo-registration;LiDAR;"pin-hole"imaging simulation;gradient mutual information
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发布时间：2021-06-10 - 摘要：Water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales,from global climate to micrometeorology. Determining water vapor distribution in the atmosphere and its changing pattern is very important. The algorithm based on some satellite remote sensors is mature（ i. e.,moderate resolution imaging spectra-radiometer（ MODIS））. Water vapor inversion algorithms based on other sensors remain in the scientific research stage or has no corresponding water vapor algorithm（ i. e.,Tropical Rainfall Measuring Mission（ TRMM） and Visible and Infrared Radiometer System（ VIRS））. TRMM/VIRS data were widely used to study precipitation. This paper uses thermal infrared split window channels at10. 8 μm and 12. 0 μm of VIRS to retrieve Precipitable Water Vapor（ PWV）. An improved physically based algorithm for the retrieval of PWV over cloud-free land surfaces was applied in this paper. First,the Split-Window Covariance-Variance Ratio（ SWCVR） method was reviewed. The surface emissivities of the two split window channels were assumed equal. Moving window method was adopted to keep the spatial resolution of the original data. Then,an operational use of this method was developed and applied to VIRS datasets. Cloud liquid water information obtained from TRMM Microwave Imager（ TMI） was used to identify the clear sky area. Given that TMI and VIRS are both mounted on the TRMM satellite,the data obtained by the two instruments are consistent in time and space and avoid data match problems. A total of 2000 radiosonde profiles were input into MODTRAN to simulate the brightness temperatures under the configuration of VIRS and find the relationship between the transmittance of the two split window channels and PWV. The profiles used in this research were randomly selected from the land sounding sites around the world to represent all kinds of water vapor condition and types of surface. The profiles were evenly distributed in the four seasons,although winter has slightly fewer profiles. Compared with the GPS results,the root mean square error of the results is 5. 76 mm,and the bias is- 1. 2 mm for the research area. Regional consistency was found between the results obtained by MODIS and the proposed algorithm. The proposed algorithm can yield reasonable results that are accurate in most cases with a split-window technique using VIRS data. Validation results indicate that the PWV retrieved by VIRS has high precision,and has a reference meaning to China FY data for retrieving PWV based on infrared split window channels. However,the precision of the algorithm in this paper was lower than the PWV results retrieved by MODIS near infrared data. The infrared channels were sensitive to the PWV in the upper atmosphere,whereas most of the PWV existed in low atmosphere. Accuracy was almost the same between the PWV retrieved by the infrared data of MODIS and the results in this paper. Few preliminary results were obtained by the present study,but the existing algorithm can be further developed and improved to reach the degree of business.关键词：precipitable water vapor;infrared;split window;VIRS;TRMM
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发布时间：2021-06-10 - 摘要：Image segmentation is a significant step in image processing. High-resolution remote sensing images can clearly characterize landscape information and eliminate the membership uncertainty caused by mixed pixels. It has considerable advantages and potential in precise segmentation. Nonetheless,the spatial complexity caused by spectral measurement and the enhanced differences among pixels in the same object may reduce segmentation result accuracy. Thus,this study establishes an interval type-2fuzzy model for detected images. Interval type-2 fuzzy theory considers the main and the secondary membership functions; the latter is represented by the label"membership 1"to express the uncertainty of pixel membership and of a segmentation decision. The aforementioned problem is solved effectively with the proposed method. The drop type problem in type-2 fuzzy theory is anotherfocus of research in this field; a fuzzy decision model is established by reducing the type-2 fuzzy model to a type-1 model. The fuzzy decision model directly influences segmentation accuracy,and recent studies are all based on the upper and lower membership functions. These models can improve decision quality to some extent; however,they do not consider the main membership function. This neglect may significantly influence decision results,particularly when the influence of neighborhood pixels cannot be incorporated into the supervised image segmentation algorithm. To overcome these shortcomings,we proposed high-resolution remote sensing image segmentation by introducing a spatial relationship into the interval type-2 fuzzy model. The proposed algorithm considers the influences of the upper,lower,and the main membership functions in establishing the fuzzy decision model.First,a type-1 fuzzy model is built with the Gauss function to characterize the uncertainty of pixel membership. Then,we extend the mean and variance of this model to construct the type-2 fuzzy model,which improves the expression of the membership function in the type-1 fuzzy model and serves as the knowledge basis to enhance segmentation decision accuracy. A segmentation decision model is then established based on the information derived from the upper,lower,and main membership functions of the trained data. Finally,the membership of a pixel is decided by the membership functions of both the pixel itself and its neighbors to optimize the segmentation of high-resolution remote sensing images.We compare this method with maximum likelihood segmentation and an interval type-2 fuzzy model segmentation without a spatial relationship via high-resolution real images. Qualitative and quantitative analysis findings indicate that the method applied in this study generates high segmentation accuracy.This study proposes a supervised image segmentation method based on an interval type-2 fuzzy model with a spatial relationship. This method improves the uncertainty expression of pixel membership,solves the problems caused by complicated spatial relevance,and enhances the accuracy of the segmentation strategy. Furthermore,the experiments show that this method is effective and feasible. In the future,the Gauss mixture model will be used as a type-1 fuzzy model to potentially improve the accurate characterization of landscape features.关键词：interval type-2 fuzzy model;trajectory of uncertainty;high resolution;remote sensing image segmentation;secondary membership function
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发布时间：2021-06-10 - 摘要：With the rapidly developing society,land use or cover change has gained considerable attention. Highly economic,practical,and efficient remote sensing technology has been used in various methods of land dynamic change detection. However,rapid image processing has become a problem with the increase in data volume and complexity in remote sensing. New complex algorithms that increase both computation volume and time have been proposed to achieve a high precision of change detection.Moreover,the Central Processing Unit（ CPU） computing cells are limited and cannot meet real-time requirements. To achieve real-time change detection using remote sensing image,this paper designs a parallel processing model based on Compute Unified Device Architecture（ CUDA）,in reference to the CVA-based change detection algorithm.The model can be divided into the following steps. To make the general PC without the large cache process data,the model first uses Geospatial Data Abstraction Library to determine image block reading,block operation,and block saving. Second,CVA change detection is paralleled through three sub-processes: changing the magnitude detection,designing the index table,and changing the direction of detection. Then,the three sub-processes are embedded in CPU and Graphic Process Unit（ GPU） through CUDA C. Finally,different sizes of multi-group images are studied with the same model to execute CVA change detection in consideration of the effect of image data volume and block size on the change detection efficiency. For comparison,the same group image data are also processed using Open MP on multi-core systems.In consideration of image data volume,the change detection speedup remains unchanged if the data volume is less than the total PC cache. Executing image block is already unnecessary. However,if the data volume is larger than the total PC cache,image block processing is needed to ensure that the cache is not out. Larger image block means more efficient change detection.The efficiency of the parallel computing of CVA-based change detection is increased 10 times in GPU than serial processing in CPU. However,Open MP is only about three times faster than serial processing in CPU. GPU is more capable in digital image processing than CPU.Change detection processing is serial between the block and image block,and processing is parallel in each image block. With enough cache,larger image block means higher degree of parallelization and change detection efficiency. Parallel operation integrated with CUDA effectively improves change detection based on CVA. To some extent,this operation reaches the effect of the real-time change detection.关键词：remote sensing imagery;change detection;change vector analysis;parallel computing;compute unified device architecture
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发布时间：2021-06-10 - 摘要：Change detection determines changes in multitemporal images,which are widely used in deforestation,land use,and urban expansion,among others. Nonetheless,traditional pixel-based change detection methods cause confusion when used on highspatial images,and generat salt-and-pepper noises on the changed map because of the presence of heterogeneous objects at a pixel level. The useful spatial or contextual information regarding the values of proximate pixels is typically ignored in a pixel-based method; therefore,an object-based method is a new approach to solve these problems in high-spatial resolution images. This study proposes a multilevel object-based method to detect changes in such images. First,we utilize the mean-shift segmentation method to segment the image and consider the heterogeneity of ground objects. Geographic objects are acquired from the segment results at different levels through multilevel information. Then,we combine the gray data of each geographic object at different scale levels to build a feature vector. The change vector analysis method is used to construct an intensity difference map at the multitemporal phase. The change results are generated by applying the expectation maximization algorithm to automatically obtain the thresholds for changed and unchanged areas; moreover,the multiscale change detection algorithm is verified with Quick Bird images of urban and rural areas. The pixel-based,single scale level object-based,and multiscale level object-based methods are also compared with one another based on these two datasets. Results show that the change detection accuracy of the object-based change detection method is higher than that of the pixel-based approach in both urban and rural areas. When the total scale level number considered in the change detection method is fixed,the multiscale,object-based method always performs better than the single level objectbased method. The change detection accuracies in the urban and rural areas are 87. 57% and 81. 55%,respectively. Furthermore,the qualitative analysis findings related to the change-detection maps suggest that the proposed technique induces high fidelity in both homogenous and small or border regions. Thus,the proposed algorithm can satisfy the requirements of change detection in urban and rural areas,which benefits land and resource monitoring.关键词：change detection;multilevel image segment;geographic object-based;high-resolution image;mean-shift;change vector analysis
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发布时间：2021-06-10 - 摘要：The presence of urban shadows in optical satellite images with high spatial resolution limits the application of remote sensing technology in urban areas. These shadows can misrepresent image information,thereby generating potential errors in the derivation of surface parameters such as surface reflectance and reflectance-based indices. Thus,these shadows must be corrected and their radiance information restored to improve the effectiveness of remote sensing images. Many shadow correction methods have been developed according to the complex statistical relationships between shadowed and sunlit areas because the former maintains weak spectral radiance information. In addition,another physical relationship has often been detected between shadowed and sunlit areas,namely,the reflectance equality relationship（ RER）. This relationship can be regarded as the reflectance of the fact that similar-type features can be identical in both a shadowed area and its nearby sunlit area under the Lambertian surface condition.RER is generally independent of shadow detection processing; nonetheless,this relationship has not been fully considered in the development of shadow restoration algorithms. In this study,an RER-based（ RERB） method were derived to correct the shadowed areas in optical multispectral satellite imageries of urban areas according to the principles of radiance transfer processes. This approach reduces the number of parameters; thus,it can lower the risk of errors propagated by the uncertainties of additional parameters. The new RERB method is tested via Geo Eye-1 and Quick Bird multispectral imageries with high spatial resolution in two different urban areas（ Beijing and Enschede） that exhibit many urban building shadows. As per a comparison of this method with the widely used mean and variance transformation method,the former can restore the colors,texture,tone,and brightness of the shadowed areas in the image to a visually satisfactory level. Quantitative analysis results suggest that the RERB method can help restore the reflectance of shadowed asphalt roads accurately,with a mean error of 7%. This method can also be used to effectively restore the spectral shape information on shadowed features; this information is particularly important when the RERB method is applied to restore multispectral imagery for classifying an image based on spectral information and band indices. Another RERB shadow correction strategy that restores shadow surface reflectance instead of apparent radiance is discussed as well; nonetheless,this strategy requires further study because much auxiliary data is needed.关键词：radiance processing;shadow removal;shadow restoration;building shadow;atmospheric correction;radiance restoration;spectral features
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发布时间：2021-06-10 - 摘要：This paper analyzed changes in the Amazon rainforest from 1982 to 2012 by combining GLASS LAI data with a global ecological classification map published by FAO in 2000. This approach harnesses the advantages of previous studies in terms of precision and time resolution. Moreover,the combination of single-point and analytical methods in the present work can fully reflect the changing status of rainforest vegetation. Previous studies regarded the entire area of South America as a study area; by contrast,the present research applies dynamic and static boundaries and considers the changes in the dynamic range of the rainforest. At the same time,the changes in the targeted research area are emphasized. Results show that the leaf area index of the Amazon rainforest fluctuated over a period of 31 years; after 2000,this index first decreased first and then increased smoothly. In terms of spatial distribution,the leaf area index of the tropical rainforest in Brazil and of other parts of the rainforest margins declined significantly over the same period. Meanwhile,the boundary in the southeast shrank constantly because of the deforestation attributed to human development activities,and the leaf area index of the rainforest interior increased considerably because of global warming. The results of the present work were consistent with those of previous studies; therefore,the current study shows that GLASS LAI,which is subject to the independent intellectual rights of China,can be used to monitor the long-term status of surface vegetation in large areas.关键词：Amazon rainforest;LAI;remote sensing;ecological environment;change monitoring
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发布时间：2021-06-10 - 摘要：<正>2015年12月5日《遥感学报》收到EI Compendex评估办公室的正式通知,经过评估中心的严格评审,《遥感学报》自2016年起被EI Compendex数据库收录,成为迄今国内唯一一本被EI Compendex数据库收录的遥感类期刊。截至目前,《遥感学报》已被EI Compendex、荷兰斯高帕斯数据库（Scopus）、EBSCO数据库、俄罗斯文摘杂志（AJ）、波兰哥白尼索引（IC）、日本科学技术文献数据库（JST）、美国乌利希期刊指南（UPD）、美国剑桥
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发布时间：2021-06-10
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