Traditional methods of extracting the ocean wave eddy information from remotely sensed imagery mainly use the edge detection technology such as Canny and Hough operators. However, due to the complexities of ocean eddi...Traditional methods of extracting the ocean wave eddy information from remotely sensed imagery mainly use the edge detection technology such as Canny and Hough operators. However, due to the complexities of ocean eddies and image itself, it is sometimes difficult to successfully detect ocean eddies using these methods. A mnltifractal filtering technology is proposed for extraction of ocean eddies and demonstrated using NASA MODIS, SeaWiFS and NOAA satellite data set in the typical area, such as ocean west boundary current. Results showed that the new method has a superior performance over the traditional methods.展开更多
Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose ...Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.展开更多
When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in inco...When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.展开更多
UAV remote sensing images have the advantages of high spatial resolution,fast speed,strong real-time performance,and convenient operation,etc.,and have become a recently developed,vital means of acquiring surface info...UAV remote sensing images have the advantages of high spatial resolution,fast speed,strong real-time performance,and convenient operation,etc.,and have become a recently developed,vital means of acquiring surface information.It is an important research task for precision agriculture to make full use of the spectrum,texture,color and other characteristic information of crops,especially the spatial arrangement and structure information of features,to explore effective methods for the classification of multiple varieties of crops.In order to explore the applicability of the object-oriented method to achieve accurate classification of UAV high-resolution images,the paper used the object-oriented classification method in ENVI to classify the UAV high-resolution remote sensing image obtained from the orderly structured 28 species of crops in the test field,which mainly includes image segmentation and object classification.The results showed that the plots obtained after classification were continuous and complete,basically in line with the actual situation,and the overall accuracy of crop classification was 91.73%,with Kappa coefficient of 0.87.Compared with the crop planting area based on remote sensing interpretation and field survey,the area error of 17 species of crops in this study was controlled within 15%,which provides a basis for object-oriented crop classification of UAV remote sensing images.展开更多
Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated fro...Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-series MODIS NDVI data at 250 m resolution.The denoising approach improved original MODIS NDVI product significantly in several periods,which may affect the accuracy of classification.The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation,statistical data and high resolution image.The field investigation accuracy was 85% at pixel level.At county-level,for winter wheat,there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814,p < 0.01).Moreover,the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period.The overall accuracy achieved 91.01%.The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.展开更多
The 5.12 Wenchuan Earthquake and the subsequent rainstorms induced a large number of landslides, which later were transformed into debris flows. To evaluate the effect of the earthquake on the sediment supply of debri...The 5.12 Wenchuan Earthquake and the subsequent rainstorms induced a large number of landslides, which later were transformed into debris flows. To evaluate the effect of the earthquake on the sediment supply of debris flows, eight debris flow basins near Beichuan City, Sichuan Province, China were chosen as the study area. The area variations of the debris flow source after the Wenchuan Earthquake and the subsequent rainstorm are analyzed and discussed in this paper. Interpretations of aerial photographs (after the 5.12 Wenchuan Earthquake) and SPOT5 images (after the rainstorm event of September 24, 2008) as well as field investigations were compared to identify the transformation of landslide surface in the study area, indicating that the landslide area in the eight debris flow basins significantly increased. The loose sediment area on the channel bed increased after the rainstorm event. In order to estimate the relationship of the landslide area with the rainfall intensity in different return periods, a model proposed by Uchihugi was adopted. Results show that new landslide area induced by heavy rainfall with 50-year and 100-year return period will be 0.87 km2 and 1.67 km2, respectively. The study results show the Wenchuan earthquake had particular influences on subsequent rainfall-induced debris flow occurrence.展开更多
As a promising technique to enhance the spatial reso- lution of remote sensing imagery, sub-pixel mapping is processed based on the spatial dependence theory with the assumption that the land cover is spatially depend...As a promising technique to enhance the spatial reso- lution of remote sensing imagery, sub-pixel mapping is processed based on the spatial dependence theory with the assumption that the land cover is spatially dependent both within pixels and be- tween them. The spatial attraction is used as a tool to describe the dependence. First, the spatial attractions between pixels, sub- pixel/pixel spatial attraction model (SPSAM), are described by the modified SPSAM (MSPSAM) that estimates the attractions accord- ing to the distribution of sub-pixels within neighboring pixels. Then a mixed spatial attraction model (MSAM) for sub-pixel mapping is proposed that integrates the spatial attractions both within pix- els and between them. According to the expression of the MSAM maximumising the spatial attraction, the genetic algorithm is em- ployed to search the optimum solution and generate the sub-pixel mapping results. Experiments show that compared with SPSAM, MSPSAM and pixel swapping algorithm modified by initialization from SPSAM (MPS), MSAM can provide higher accuracy and more rational sub-pixel mapping results.展开更多
Poverty is a severe barrier to sustainable human development and a pressing worldwide issue.Understanding how to accurately assess the spatial distribution of poverty in mountain areas has become crucial for ensuring ...Poverty is a severe barrier to sustainable human development and a pressing worldwide issue.Understanding how to accurately assess the spatial distribution of poverty in mountain areas has become crucial for ensuring that governments at all levels take suitable poverty reduction strategies.In this study,the mountain poverty spatial index(MPSI)was created by combining the digital elevation model(DEM),Luojia-1 night-time light imagery,point of interest(POI)data,and vegetation index products.The MPSI was then used to identify the spatial characteristics of poverty at different scales in the hilly area of Ganzhou city,Jiangxi Province,China.Socioeconomic statistics and Google satellite images were used to verify the reliability of MPSI by constructing a multidimensional poverty index(MPI)at the county scale.The results showed that MPSI and MPI have a positive correlation with a correlation coefficient of 0.8934(P<0.001),which indicates that MPSI could be used to identify the spatial distribution of poverty well.Specifically,the smallest distribution of both MPSI and MPI was in Zhanggong District(1.4555 and 0.1894),which indicates that most of the affluent counties were concentrated in the central region of Ganzhou,and the poor areas were scattered in the surrounding areas of Ganzhou.In addition,MPSI accurately identified poverty in mountainous areas with complex terrain in small administrative units,which can provide a more accurate way to monitor the poverty situation in the mountainous areas of China.This study will be useful for providing scientific references for the Chinese government to implement targeted strategies for eradicating poverty with differentiated policies.展开更多
Ivory Coast is a country rich in base metals and precious minerals: gold, manganese, diamond, iron, bauxite, cobalt and nickel. These natural resources are exposed to destruction and fragmentation by mining activities...Ivory Coast is a country rich in base metals and precious minerals: gold, manganese, diamond, iron, bauxite, cobalt and nickel. These natural resources are exposed to destruction and fragmentation by mining activities. The artisanal and small-scale exploitation of gold are increasingly practiced in our rural areas. These activities escape often in the control and monitoring of the mining administration. In order to better constrain these activities on the environment, the present work used remote sensing imageries to see its spatio-temporal impacts in the rural world in central Ivory Coast. The results show that gold artisanal activities have been practiced since 2013 and are experiencing an increasingly important growth. We note a devastation of forests and savannahs, a pollution of surface water, as well as an increase in poverty in rural areas. These activities are practiced near habited areas (villages). This creates a reduction of cultivatable soil. Remote sensing imageries make it possible to quickly map areas at large-scale gold mining in time and space.展开更多
High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection...High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection(BCD)and semantic change detection(SCD)simultaneously because classification datasets always have one time phase and BCD datasets focus only on the changed location,ignoring the changed classes.Public SCD datasets are rare but much needed.To solve the above problems,a tri-temporal SCD dataset made up of Gaofen-2(GF-2)remote sensing imagery(with 11 LULC classes and 60 change directions)was built in this study,namely,the Wuhan Urban Semantic Understanding(WUSU)dataset.Popular deep learning based methods for LULC classification,BCD and SCD are tested to verify the reliability of WUSU.A Siamese-based multi-task joint framework with a multi-task joint loss(MJ loss)named ChangeMJ is proposed to restore the object boundaries and obtains the best results in LULC classification,BCD and SCD,compared to the state-of-the-art(SOTA)methods.Finally,a large spatial-scale mapping for Wuhan central urban area is carried out to verify that the WUsU dataset and the ChangeMJ framework have good application values.展开更多
There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling ...There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.展开更多
Due to the atmosphere effect,the qualities of images decrease conspicuously,practically in the visible bands,in the processing of earth observation by the satellite-borne sensors.Thus,removing the atmosphere effects h...Due to the atmosphere effect,the qualities of images decrease conspicuously,practically in the visible bands,in the processing of earth observation by the satellite-borne sensors.Thus,removing the atmosphere effects has become a key step to improve the qualities of images and to retrieve the actual reflectivity of surface features.An atmospheric correction approach,called ACVSS(Atmospheric Correction based Vector Space of Spectrum),is proposed here based on the vector space of the features' spectrum.The reflectance image of each band is retrieved first according to the radiative transfer equation,then the spectrum's vector space is constructed using the infrared bands,and finally the residual errors of the reflectance images in the visible bands are corrected based on the pixel position in the spectrum's vector space.The proposed methodology is verified through atmospheric correction on Landsat-7 ETM+ imagery.The experimental results show that our method is more accurate and the corrected image is more distinct,compared with those offered by current popular atmospheric correction software.展开更多
Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological enviro...Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological environment,and regional water cycle.However,the differences in spatial-spectral characteristics of various types of plateau lakes,and the complex background information of plateau both influence the extraction effect of lakes.Therefore,it is a great challenge to completely and effectively extract plateau lakes.In this study,we proposed a multiscale contextual information aggregation network,termed MSCANet,to automatically extract Plateau lake regions.It consists of three main components:a multiscale lake feature encoder,a feature decoder,and a Multicore Pyramid Pooling Module(MPPM).The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous scenes.The MPPM module aggregated the contextual information of various lakes globally.We applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data;additionally,comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological integrity.Finally,we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet.展开更多
Poverty alleviation is one of the greatest challenges faced by low-income and middle-income countries.China,which had the largest rural poverty-stricken population,has made tremendous efforts in alleviating poverty es...Poverty alleviation is one of the greatest challenges faced by low-income and middle-income countries.China,which had the largest rural poverty-stricken population,has made tremendous efforts in alleviating poverty especially since the implementation of the targeted poverty alleviation(TPA)policy in 2014,and by 2020,all national poverty-stricken counties(NPCs)have been out of poverty.This study combines deep learning with multiple satellite datasets to estimate county-level economic develop-ment from 2008 to 2019 and assess the effect of the TPA policy for 592 national poverty-stricken counties(NPCs)at country,provincial and county levels.Per capita gross domestic product(GDP)is used to measure the affluence level.From 2014 through 2019,the 592 NPCs experience an average growth rate of per capita GDP at 7.6%±0.4%,higher than the average growth rate of 310 adjacent non-NPC counties(7.3%±0.4%)and of the whole country(6.3%).We also reveal 42 counties with weak growth recently and that the average affluence level of the NPCs in 2019 is still much lower than the national or provincial averages.The inexpensive,timely and accurate method proposed here can be applied to other low-income and middle-income countries for affluence assessment.展开更多
文摘Traditional methods of extracting the ocean wave eddy information from remotely sensed imagery mainly use the edge detection technology such as Canny and Hough operators. However, due to the complexities of ocean eddies and image itself, it is sometimes difficult to successfully detect ocean eddies using these methods. A mnltifractal filtering technology is proposed for extraction of ocean eddies and demonstrated using NASA MODIS, SeaWiFS and NOAA satellite data set in the typical area, such as ocean west boundary current. Results showed that the new method has a superior performance over the traditional methods.
基金funded by the Chongqing Normal University Startup Foundation for PhD(22XLB021)supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2023B40).
文摘Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.
基金This work was supported in part by the Key Project of Natural Science Research of Anhui Provincial Department of Education under Grant KJ2017A416in part by the Fund of National Sensor Network Engineering Technology Research Center(No.NSNC202103).
文摘When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.
基金Supported by College Students Innovation and Entrepreneurship Training Program of Jilin University(No.202010183695)。
文摘UAV remote sensing images have the advantages of high spatial resolution,fast speed,strong real-time performance,and convenient operation,etc.,and have become a recently developed,vital means of acquiring surface information.It is an important research task for precision agriculture to make full use of the spectrum,texture,color and other characteristic information of crops,especially the spatial arrangement and structure information of features,to explore effective methods for the classification of multiple varieties of crops.In order to explore the applicability of the object-oriented method to achieve accurate classification of UAV high-resolution images,the paper used the object-oriented classification method in ENVI to classify the UAV high-resolution remote sensing image obtained from the orderly structured 28 species of crops in the test field,which mainly includes image segmentation and object classification.The results showed that the plots obtained after classification were continuous and complete,basically in line with the actual situation,and the overall accuracy of crop classification was 91.73%,with Kappa coefficient of 0.87.Compared with the crop planting area based on remote sensing interpretation and field survey,the area error of 17 species of crops in this study was controlled within 15%,which provides a basis for object-oriented crop classification of UAV remote sensing images.
基金Under the auspices of Knowledge Innovation Programs of Chinese Academy of Sciences (No.KZCX2-YW-449,KSCX-YW-09)National Natural Science Foundation of China (No.40971025,40901030,50969003)
文摘Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-series MODIS NDVI data at 250 m resolution.The denoising approach improved original MODIS NDVI product significantly in several periods,which may affect the accuracy of classification.The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation,statistical data and high resolution image.The field investigation accuracy was 85% at pixel level.At county-level,for winter wheat,there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814,p < 0.01).Moreover,the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period.The overall accuracy achieved 91.01%.The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.
基金supported by Research Fund of the State Key Laboratory of Geo-Hazard Prevention (Grant SKLGP2009Z004)the National Basic Research Program of China (also called 973 Program) (Grant No. 2011CB409903)
文摘The 5.12 Wenchuan Earthquake and the subsequent rainstorms induced a large number of landslides, which later were transformed into debris flows. To evaluate the effect of the earthquake on the sediment supply of debris flows, eight debris flow basins near Beichuan City, Sichuan Province, China were chosen as the study area. The area variations of the debris flow source after the Wenchuan Earthquake and the subsequent rainstorm are analyzed and discussed in this paper. Interpretations of aerial photographs (after the 5.12 Wenchuan Earthquake) and SPOT5 images (after the rainstorm event of September 24, 2008) as well as field investigations were compared to identify the transformation of landslide surface in the study area, indicating that the landslide area in the eight debris flow basins significantly increased. The loose sediment area on the channel bed increased after the rainstorm event. In order to estimate the relationship of the landslide area with the rainfall intensity in different return periods, a model proposed by Uchihugi was adopted. Results show that new landslide area induced by heavy rainfall with 50-year and 100-year return period will be 0.87 km2 and 1.67 km2, respectively. The study results show the Wenchuan earthquake had particular influences on subsequent rainfall-induced debris flow occurrence.
基金supported by the National Natural Science Foundation of China (60802059)the Foundation for the Doctoral Program of Higher Education of China (200802171003)
文摘As a promising technique to enhance the spatial reso- lution of remote sensing imagery, sub-pixel mapping is processed based on the spatial dependence theory with the assumption that the land cover is spatially dependent both within pixels and be- tween them. The spatial attraction is used as a tool to describe the dependence. First, the spatial attractions between pixels, sub- pixel/pixel spatial attraction model (SPSAM), are described by the modified SPSAM (MSPSAM) that estimates the attractions accord- ing to the distribution of sub-pixels within neighboring pixels. Then a mixed spatial attraction model (MSAM) for sub-pixel mapping is proposed that integrates the spatial attractions both within pix- els and between them. According to the expression of the MSAM maximumising the spatial attraction, the genetic algorithm is em- ployed to search the optimum solution and generate the sub-pixel mapping results. Experiments show that compared with SPSAM, MSPSAM and pixel swapping algorithm modified by initialization from SPSAM (MPS), MSAM can provide higher accuracy and more rational sub-pixel mapping results.
基金supported by the Science and Technology Program of Jiangxi Provincial Education Department(GJJ180233)。
文摘Poverty is a severe barrier to sustainable human development and a pressing worldwide issue.Understanding how to accurately assess the spatial distribution of poverty in mountain areas has become crucial for ensuring that governments at all levels take suitable poverty reduction strategies.In this study,the mountain poverty spatial index(MPSI)was created by combining the digital elevation model(DEM),Luojia-1 night-time light imagery,point of interest(POI)data,and vegetation index products.The MPSI was then used to identify the spatial characteristics of poverty at different scales in the hilly area of Ganzhou city,Jiangxi Province,China.Socioeconomic statistics and Google satellite images were used to verify the reliability of MPSI by constructing a multidimensional poverty index(MPI)at the county scale.The results showed that MPSI and MPI have a positive correlation with a correlation coefficient of 0.8934(P<0.001),which indicates that MPSI could be used to identify the spatial distribution of poverty well.Specifically,the smallest distribution of both MPSI and MPI was in Zhanggong District(1.4555 and 0.1894),which indicates that most of the affluent counties were concentrated in the central region of Ganzhou,and the poor areas were scattered in the surrounding areas of Ganzhou.In addition,MPSI accurately identified poverty in mountainous areas with complex terrain in small administrative units,which can provide a more accurate way to monitor the poverty situation in the mountainous areas of China.This study will be useful for providing scientific references for the Chinese government to implement targeted strategies for eradicating poverty with differentiated policies.
文摘Ivory Coast is a country rich in base metals and precious minerals: gold, manganese, diamond, iron, bauxite, cobalt and nickel. These natural resources are exposed to destruction and fragmentation by mining activities. The artisanal and small-scale exploitation of gold are increasingly practiced in our rural areas. These activities escape often in the control and monitoring of the mining administration. In order to better constrain these activities on the environment, the present work used remote sensing imageries to see its spatio-temporal impacts in the rural world in central Ivory Coast. The results show that gold artisanal activities have been practiced since 2013 and are experiencing an increasingly important growth. We note a devastation of forests and savannahs, a pollution of surface water, as well as an increase in poverty in rural areas. These activities are practiced near habited areas (villages). This creates a reduction of cultivatable soil. Remote sensing imageries make it possible to quickly map areas at large-scale gold mining in time and space.
基金supported by National Key Research and Development Program of China under grant number 2022YFB3903404National Natural Science Foundation of China under grant number 42325105,42071350LIESMARS Special Research Funding.
文摘High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection(BCD)and semantic change detection(SCD)simultaneously because classification datasets always have one time phase and BCD datasets focus only on the changed location,ignoring the changed classes.Public SCD datasets are rare but much needed.To solve the above problems,a tri-temporal SCD dataset made up of Gaofen-2(GF-2)remote sensing imagery(with 11 LULC classes and 60 change directions)was built in this study,namely,the Wuhan Urban Semantic Understanding(WUSU)dataset.Popular deep learning based methods for LULC classification,BCD and SCD are tested to verify the reliability of WUSU.A Siamese-based multi-task joint framework with a multi-task joint loss(MJ loss)named ChangeMJ is proposed to restore the object boundaries and obtains the best results in LULC classification,BCD and SCD,compared to the state-of-the-art(SOTA)methods.Finally,a large spatial-scale mapping for Wuhan central urban area is carried out to verify that the WUsU dataset and the ChangeMJ framework have good application values.
基金supported by the National Natural Science Foundation of China(Grant Nos.41272359&11001019)the Specialized Research Fund for the Doctoral Program of Higher Education(SRFDP)the Fundamental Research Funds for the Central Universities
文摘There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.
基金supported by National High-tech R&D Program (Grant Nos.2011AA120300,2011AA120302)Foster-ing Program of Science and Technology Innovative Platform,Northeast Normal University (Grant No.106111065202)
文摘Due to the atmosphere effect,the qualities of images decrease conspicuously,practically in the visible bands,in the processing of earth observation by the satellite-borne sensors.Thus,removing the atmosphere effects has become a key step to improve the qualities of images and to retrieve the actual reflectivity of surface features.An atmospheric correction approach,called ACVSS(Atmospheric Correction based Vector Space of Spectrum),is proposed here based on the vector space of the features' spectrum.The reflectance image of each band is retrieved first according to the radiative transfer equation,then the spectrum's vector space is constructed using the infrared bands,and finally the residual errors of the reflectance images in the visible bands are corrected based on the pixel position in the spectrum's vector space.The proposed methodology is verified through atmospheric correction on Landsat-7 ETM+ imagery.The experimental results show that our method is more accurate and the corrected image is more distinct,compared with those offered by current popular atmospheric correction software.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research(STEP)program under Grant 2019QZKK0106the Science and Technology Major Project of Henan Province under Grant 201400210900.
文摘Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological environment,and regional water cycle.However,the differences in spatial-spectral characteristics of various types of plateau lakes,and the complex background information of plateau both influence the extraction effect of lakes.Therefore,it is a great challenge to completely and effectively extract plateau lakes.In this study,we proposed a multiscale contextual information aggregation network,termed MSCANet,to automatically extract Plateau lake regions.It consists of three main components:a multiscale lake feature encoder,a feature decoder,and a Multicore Pyramid Pooling Module(MPPM).The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous scenes.The MPPM module aggregated the contextual information of various lakes globally.We applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data;additionally,comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological integrity.Finally,we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet.
基金This work was supported by the National Natural Science Foundation of China under Grant No.41925006.
文摘Poverty alleviation is one of the greatest challenges faced by low-income and middle-income countries.China,which had the largest rural poverty-stricken population,has made tremendous efforts in alleviating poverty especially since the implementation of the targeted poverty alleviation(TPA)policy in 2014,and by 2020,all national poverty-stricken counties(NPCs)have been out of poverty.This study combines deep learning with multiple satellite datasets to estimate county-level economic develop-ment from 2008 to 2019 and assess the effect of the TPA policy for 592 national poverty-stricken counties(NPCs)at country,provincial and county levels.Per capita gross domestic product(GDP)is used to measure the affluence level.From 2014 through 2019,the 592 NPCs experience an average growth rate of per capita GDP at 7.6%±0.4%,higher than the average growth rate of 310 adjacent non-NPC counties(7.3%±0.4%)and of the whole country(6.3%).We also reveal 42 counties with weak growth recently and that the average affluence level of the NPCs in 2019 is still much lower than the national or provincial averages.The inexpensive,timely and accurate method proposed here can be applied to other low-income and middle-income countries for affluence assessment.