Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satell...Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies,the mix of high-and low-resolution images that make object classification difficult because of the mixed pixel problem.Accuracy is impacted by the mixed pixel problem,which occurs when pixels belong to different classes and makes detection challenging.To identify mixed pixels,Band Math is used to merge numerous bands to generate a new band NDVI.Thresholding is used to analyze the edges of deforested and non-deforested areas.Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas.Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023.Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues;compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels.With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area.The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.展开更多
A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It is a simplex-based method which extracts endmembers sequentially using computations of largest si...A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It is a simplex-based method which extracts endmembers sequentially using computations of largest simplex volumes. At each searching step of this extraction algorithm, searching for the simplex with the largest volume is equivalent to searching for a new orthogonal basis which has the largest norm. The new endmember corresponds to the new basis with the largest norm. This algorithm runs very fast and can also avoid the dilemma in traditional simplex-based endmember extraction algorithms, such as N-FINDR, that it generally produces different sets of final endmembers if different initial conditions are used. Moreover, with this set of orthogonal bases, the proposed algorithm can also determine the proper number of endmembers and finish the unmixing of the original images which the traditional simplex-based algorithms cannot do by themselves. Experimental results of both artificial simulated images and practical remote sensing images demonstrate the algorithm proposed in this paper is a fast and accurate algorithm for the decomposition of mixed pixels.展开更多
MODIS （Moderate Resolution Imaging Spectroradiometer） is a key instrument aboard the Terra （EOS AM） and Aqua （EOS PM） satellites. Linear spectral mixture models are applied to MOIDS data for the sub-pixel classi...MODIS （Moderate Resolution Imaging Spectroradiometer） is a key instrument aboard the Terra （EOS AM） and Aqua （EOS PM） satellites. Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers. Shaoxing county of Zhejiang Province in China was chosen to be the study site and early rice was selected as the study crop. The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classification derived from TM data acquired on the same day, which implies that MODIS data could be used as satellite data source for rice cultivation area estimation, possibly rice growth monitoring and yield forecasting on the regional scale.展开更多
Oil spilled on the sea ice surface in the Bohai Sea of China is studied through the field measurements of the reflectance of a simulated sea ice-oil film mixed pixel. The reflection characteristics of sea ice and oil ...Oil spilled on the sea ice surface in the Bohai Sea of China is studied through the field measurements of the reflectance of a simulated sea ice-oil film mixed pixel. The reflection characteristics of sea ice and oil film are also analyzed. It is found that the mixed pixel of sea ice and oil film is a linear mixed pixel. The means of extracting sea ice pixels containing oil film is presented using a double-band ratio oil-film sea-ice index（DROSI） and a normalized difference oil-film sea-ice index（NDOSI） through the analysis of the reflectance curves of the sea iceoil film pixel for different ratios of oil film. The area proportion of the oil film in the sea ice-oil film pixel can be accurately estimated by the average reflectance of the band of 1 610–1 630 nm, and the volume of the spilled oil can be further estimated. The method of the sea ice-oil film pixel extraction and the models to estimate the proportion of oil film area in the sea ice-oil film pixel can be applied to the oil spill monitoring of the ice-covered area in the Bohai Sea using multispectral or hyperspectral remote sensing images in the shortwave infrared band（1 500–1 780 nm）.展开更多
Desert lakes are important wetland resources in the blown-sand area of western China and play a significant role in maintain-ing the regional ecological environment.However,large-scale coal mining in recent years has ...Desert lakes are important wetland resources in the blown-sand area of western China and play a significant role in maintain-ing the regional ecological environment.However,large-scale coal mining in recent years has considerably impacted the deposition condition of several lakes.Rapid and accurate extraction of lake information based on satellite images is crucial for developing protective measures against desertification.However,the spatial resolution of these images often leads to mixed pixels near water boundaries,affecting extraction precision.Traditional pixel unmixing methods mainly obtain water coverage information in a mixed pixel,making it difficult to accurately describe the spatial distribution.In this paper,the cellular automata(CA)model was adopted in order to realize lake information extraction at a sub-pixel level.A mining area in Shenmu City,Shaanxi Province,China is selected as the research region,using the image of Sentinel-2 as the data source and the high spatial resolution UAV image as the reference.First,water coverage of mixed pixels in the Sentinel-2 image was calculated with the dimidiate pixel model and the fully constrained least squares(FCLS)method.Second,the mixed pixels were subdivided to form the cellular space at a sub-pixel level and the transition rules are constructed based on the water coverage information and spatial correlation.Lastly,the process was implemented using Python and IDL,with the ArcGIS and ENVI software being used for validation.The experiments show that the CA model can improve the sub-pixel positioning accuracy for lake bodies in mixed pixel image and improve classification accuracy.The FCLS-CA model has a higher accuracy and is able to identify most water bodies in the study area,and is therefore suitable for desert lake monitor-ing in mining areas.展开更多
In hyperspectral image classification(HSIC),accurately extracting spatial and spectral information from hyperspectral images(HSI)is crucial for achieving precise classification.However,due to low spatial resolution an...In hyperspectral image classification(HSIC),accurately extracting spatial and spectral information from hyperspectral images(HSI)is crucial for achieving precise classification.However,due to low spatial resolution and complex category boundary,mixed pixels containing features from multiple classes are inevitable in HSIs.Additionally,the spectral similarity among different classes challenge for extracting distinctive spectral features essential for HSIC.To address the impact of mixed pixels and spectral similarity for HSIC,we propose a central-pixel guiding sub-pixel and sub-channel convolution network(CP-SPSC)to extract more precise spatial and spectral features.Firstly,we designed spatial attention(CP-SPA)and spectral attention(CP-SPE)informed by the central pixel to effectively reduce spectral interference of irrelevant categories in the same patch.Furthermore,we use CP-SPA to guide 2D sub-pixel convolution(SPConv2d)to capture spatial features finer than the pixel level.Meanwhile,CP-SPE is also utilized to guide 1D sub-channel con-volution(SCConv1d)in selecting more precise spectral channels.For fusing spatial and spectral information at the feature-level,the spectral feature extension transformation module(SFET)adopts mirror-padding and snake permutation to transform 1D spectral information of the center pixel into 2D spectral features.Experiments on three popular datasets demonstrate that ours out-performs several state-of-the-art methods in accuracy.展开更多
文摘Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies,the mix of high-and low-resolution images that make object classification difficult because of the mixed pixel problem.Accuracy is impacted by the mixed pixel problem,which occurs when pixels belong to different classes and makes detection challenging.To identify mixed pixels,Band Math is used to merge numerous bands to generate a new band NDVI.Thresholding is used to analyze the edges of deforested and non-deforested areas.Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas.Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023.Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues;compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels.With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area.The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.
基金Supported in part by the National Natural Science Foundation of China (Grant No. 60672116)the National High-Tech Research & Development Program of China (Grant No. 2009AA12Z115)the Shanghai Leading Academic Discipline Project (Grant No. B112)
文摘A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It is a simplex-based method which extracts endmembers sequentially using computations of largest simplex volumes. At each searching step of this extraction algorithm, searching for the simplex with the largest volume is equivalent to searching for a new orthogonal basis which has the largest norm. The new endmember corresponds to the new basis with the largest norm. This algorithm runs very fast and can also avoid the dilemma in traditional simplex-based endmember extraction algorithms, such as N-FINDR, that it generally produces different sets of final endmembers if different initial conditions are used. Moreover, with this set of orthogonal bases, the proposed algorithm can also determine the proper number of endmembers and finish the unmixing of the original images which the traditional simplex-based algorithms cannot do by themselves. Experimental results of both artificial simulated images and practical remote sensing images demonstrate the algorithm proposed in this paper is a fast and accurate algorithm for the decomposition of mixed pixels.
文摘MODIS （Moderate Resolution Imaging Spectroradiometer） is a key instrument aboard the Terra （EOS AM） and Aqua （EOS PM） satellites. Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers. Shaoxing county of Zhejiang Province in China was chosen to be the study site and early rice was selected as the study crop. The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classification derived from TM data acquired on the same day, which implies that MODIS data could be used as satellite data source for rice cultivation area estimation, possibly rice growth monitoring and yield forecasting on the regional scale.
基金The National High Technology Research and Development Program(863 Program)of China under contract No.2011AA100505the Projects of the State Key Laboratory of Earth Surface Progresses and Resource Ecology,Beijing Normal University of China under contract No.2010-KF-08
文摘Oil spilled on the sea ice surface in the Bohai Sea of China is studied through the field measurements of the reflectance of a simulated sea ice-oil film mixed pixel. The reflection characteristics of sea ice and oil film are also analyzed. It is found that the mixed pixel of sea ice and oil film is a linear mixed pixel. The means of extracting sea ice pixels containing oil film is presented using a double-band ratio oil-film sea-ice index（DROSI） and a normalized difference oil-film sea-ice index（NDOSI） through the analysis of the reflectance curves of the sea iceoil film pixel for different ratios of oil film. The area proportion of the oil film in the sea ice-oil film pixel can be accurately estimated by the average reflectance of the band of 1 610–1 630 nm, and the volume of the spilled oil can be further estimated. The method of the sea ice-oil film pixel extraction and the models to estimate the proportion of oil film area in the sea ice-oil film pixel can be applied to the oil spill monitoring of the ice-covered area in the Bohai Sea using multispectral or hyperspectral remote sensing images in the shortwave infrared band（1 500–1 780 nm）.
基金supported by the Shaanxi Province Soft Science Research Program (2022KRM034).
文摘Desert lakes are important wetland resources in the blown-sand area of western China and play a significant role in maintain-ing the regional ecological environment.However,large-scale coal mining in recent years has considerably impacted the deposition condition of several lakes.Rapid and accurate extraction of lake information based on satellite images is crucial for developing protective measures against desertification.However,the spatial resolution of these images often leads to mixed pixels near water boundaries,affecting extraction precision.Traditional pixel unmixing methods mainly obtain water coverage information in a mixed pixel,making it difficult to accurately describe the spatial distribution.In this paper,the cellular automata(CA)model was adopted in order to realize lake information extraction at a sub-pixel level.A mining area in Shenmu City,Shaanxi Province,China is selected as the research region,using the image of Sentinel-2 as the data source and the high spatial resolution UAV image as the reference.First,water coverage of mixed pixels in the Sentinel-2 image was calculated with the dimidiate pixel model and the fully constrained least squares(FCLS)method.Second,the mixed pixels were subdivided to form the cellular space at a sub-pixel level and the transition rules are constructed based on the water coverage information and spatial correlation.Lastly,the process was implemented using Python and IDL,with the ArcGIS and ENVI software being used for validation.The experiments show that the CA model can improve the sub-pixel positioning accuracy for lake bodies in mixed pixel image and improve classification accuracy.The FCLS-CA model has a higher accuracy and is able to identify most water bodies in the study area,and is therefore suitable for desert lake monitor-ing in mining areas.
基金supported by the National Natural Science Foundation of China(No.62071323).
文摘In hyperspectral image classification(HSIC),accurately extracting spatial and spectral information from hyperspectral images(HSI)is crucial for achieving precise classification.However,due to low spatial resolution and complex category boundary,mixed pixels containing features from multiple classes are inevitable in HSIs.Additionally,the spectral similarity among different classes challenge for extracting distinctive spectral features essential for HSIC.To address the impact of mixed pixels and spectral similarity for HSIC,we propose a central-pixel guiding sub-pixel and sub-channel convolution network(CP-SPSC)to extract more precise spatial and spectral features.Firstly,we designed spatial attention(CP-SPA)and spectral attention(CP-SPE)informed by the central pixel to effectively reduce spectral interference of irrelevant categories in the same patch.Furthermore,we use CP-SPA to guide 2D sub-pixel convolution(SPConv2d)to capture spatial features finer than the pixel level.Meanwhile,CP-SPE is also utilized to guide 1D sub-channel con-volution(SCConv1d)in selecting more precise spectral channels.For fusing spatial and spectral information at the feature-level,the spectral feature extension transformation module(SFET)adopts mirror-padding and snake permutation to transform 1D spectral information of the center pixel into 2D spectral features.Experiments on three popular datasets demonstrate that ours out-performs several state-of-the-art methods in accuracy.
