Remote sensing image segmentation has a wide range of applications in land cover classification,urban building recognition,crop monitoring,and other fields.In recent years,with the booming development of deep learning...Remote sensing image segmentation has a wide range of applications in land cover classification,urban building recognition,crop monitoring,and other fields.In recent years,with the booming development of deep learning,remote sensing image segmentation models based on deep learning have gradually emerged and produced a large number of scientific research achievements.This article is based on deep learning and reviews the latest achievements in remote sensing image segmentation,exploring future development directions.Firstly,the basic concepts,characteristics,classification,tasks,and commonly used datasets of remote sensingimages are presented.Secondly,the segmentation models based on deep learning were classified and summarized,and the principles,characteristics,and applications of various models were presented.Then,the key technologies involved in deep learning remote sensing image segmentation were introduced.Finally,the future development direction and applicationprospects of remote sensing image segmentation were discussed.This article reviews the latest research achievements in remote sensing image segmentationfrom the perspective of deep learning,which can provide reference and inspiration for the research of remote sensing image segmentation.展开更多
U-Net has been widely applied in semantic segmentation tasks,but it faces challenges in the semantic segmentation of high-resolution remote sensing images due to the loss of boundary information during the downsamplin...U-Net has been widely applied in semantic segmentation tasks,but it faces challenges in the semantic segmentation of high-resolution remote sensing images due to the loss of boundary information during the downsampling process and the inherent blurriness of object boundaries in remote sensing images.We propose an advanced U-Net variant model that addresses these issues.By introducing the CBAM attention mechanism,we enhance the extraction of boundary information during the downsampling process,and by incorporating a cascaded edge detection module,we significantly improve the model’s boundary segmentation performance.As a result,the model demonstrates excellent performance in the segmentation of high-resolution remote sensing images.The results indicate that our proposed model outperforms other baseline models and exhibits superior performance.展开更多
文摘Remote sensing image segmentation has a wide range of applications in land cover classification,urban building recognition,crop monitoring,and other fields.In recent years,with the booming development of deep learning,remote sensing image segmentation models based on deep learning have gradually emerged and produced a large number of scientific research achievements.This article is based on deep learning and reviews the latest achievements in remote sensing image segmentation,exploring future development directions.Firstly,the basic concepts,characteristics,classification,tasks,and commonly used datasets of remote sensingimages are presented.Secondly,the segmentation models based on deep learning were classified and summarized,and the principles,characteristics,and applications of various models were presented.Then,the key technologies involved in deep learning remote sensing image segmentation were introduced.Finally,the future development direction and applicationprospects of remote sensing image segmentation were discussed.This article reviews the latest research achievements in remote sensing image segmentationfrom the perspective of deep learning,which can provide reference and inspiration for the research of remote sensing image segmentation.
文摘U-Net has been widely applied in semantic segmentation tasks,but it faces challenges in the semantic segmentation of high-resolution remote sensing images due to the loss of boundary information during the downsampling process and the inherent blurriness of object boundaries in remote sensing images.We propose an advanced U-Net variant model that addresses these issues.By introducing the CBAM attention mechanism,we enhance the extraction of boundary information during the downsampling process,and by incorporating a cascaded edge detection module,we significantly improve the model’s boundary segmentation performance.As a result,the model demonstrates excellent performance in the segmentation of high-resolution remote sensing images.The results indicate that our proposed model outperforms other baseline models and exhibits superior performance.
