Research On Cloud Shadow Image Segmentation Based On Multi-scale Fusio

Cloud and cloud shadow detection is a crucial issue in remote sensing image processing.The backgrounds of clouds and cloud shadows are mostly complex in actual remote sensing images.Traditional methods are easily affected by ground object interference,noise interference and other factors,and problems such as missing detection and false detection are prone to occur in the process of cloud detection.In addition,due to insufficient edge information extraction capabilities,traditional methods have very rough segmentation results for cloud and cloud shadow boundaries.To solve the above problems,this paper first proposes a Multi-scale Strip Pooling Feature Aggregation Network.This method uses the residual network as the backbone to extract different levels of semantic information.In order to improve the multi-scale information extraction ability of the network,an Improved Pyramid Pooling module is introduced to mine deep multi-scale semantic information.Then,the Mutual Fusion module is used to guide the fusion of different levels of information.Finally,in view of the problem of rough segmentation boundaries in traditional methods,the Strip Boundary Refinement module is used to repair the boundary information of clouds and cloud shadows.In addition,considering the limited receptive field of Convolution Neural Networks,a dual-branch network composed of Transformer and convolution network is proposed to extract semantic and spatial detail information of the image respectively to solve the problems of false detection and missed detection.To improve the model’s feature extraction,a Mutual Guidance Module is introduced so that the Transformer Branch and the Convolution Branch can guide each other for feature mining.Finally,in view of the problem of rough segmentation boundary,this work uses different features extracted by the Transformer Branch and the Convolution Branch for decoding,and repairs the rough segmentation boundary in the decoding part to make the segmentation boundary clearer.The experimental results show that the two models proposed in this paper have achieved good results in many remote sensing image cloud and cloud shadow segmentation data sets.