Based on low-altitude remote sensing images,this paper established sample set of typical river vegetation elements and proposed river vegetation extraction technical solution to adaptively extract typical vegetation e...Based on low-altitude remote sensing images,this paper established sample set of typical river vegetation elements and proposed river vegetation extraction technical solution to adaptively extract typical vegetation elements of river basins.The main research of this paper were as follows:(1)a typical vegetation extraction sample set based on low-altitude remote sensing images was established.(2)A low-altitude remote sensing image vegetation extraction model based on the focus perception module was designed to realize the end-to-end automatic extraction of different types of vegetation areas of low-altitude remote sensing images to fully learn the spectral spatial texture information and deep semantic information of the images.(3)By comparison with the baseline method,baseline method with embedded focus perception module showed an improvement in the precision by 7.37%and mIoU by 49.49%.Through visual interpretation and quantitative calculation analysis,the typical river vegetation adaptive extraction network has effectiveness and generalization ability,consistent with the needs of practical applications of vegetation extraction.展开更多
文摘Based on low-altitude remote sensing images,this paper established sample set of typical river vegetation elements and proposed river vegetation extraction technical solution to adaptively extract typical vegetation elements of river basins.The main research of this paper were as follows:(1)a typical vegetation extraction sample set based on low-altitude remote sensing images was established.(2)A low-altitude remote sensing image vegetation extraction model based on the focus perception module was designed to realize the end-to-end automatic extraction of different types of vegetation areas of low-altitude remote sensing images to fully learn the spectral spatial texture information and deep semantic information of the images.(3)By comparison with the baseline method,baseline method with embedded focus perception module showed an improvement in the precision by 7.37%and mIoU by 49.49%.Through visual interpretation and quantitative calculation analysis,the typical river vegetation adaptive extraction network has effectiveness and generalization ability,consistent with the needs of practical applications of vegetation extraction.
