Improved Segformer network semantic segmentation method for remote sensing images based on attention mechanism
In view of the problems of local Receptive field limitation and deep semantic feature loss when Segform-er processes remote sensing images with complex spatial and spectral characteristics,a multi-level layered encoder network structure with different attention modules embedded between Segformer modules at different levels is proposed:polarization attention module PSA is embedded before Block2,To enhance the network's spatial perception of large-scale features,alleviate feature semantic loss,and embed efficient channel attention module ECA before Block3 and Block4 to obtain weighted features of the channel,thereby enhancing the network's recognition and perception ability of important features,and ultimately achieving pixel level semantic segmentation of remote sensing images through multi-ple feature cascades.Through testing on the GID and BCDD datasets,compared to the original Segformer,the new network has increased mIOU(％)by 1.85％and 1.63％,respectively,in both datasets.
NETL
NSTL
万方数据
