A semantic segmentation method for remote sensing images based on multiple contextual feature extraction

Semantic segmentation of remote sensing images plays a significant role in many applications such as urban planning and ecological protection, but its semantic segmentation suffers from large intra-category variation and large differences in the scale of objects, so it is prone to misclassification. To cope with this issue, an embedded channel's categorical attention module (ECCA) is proposed to extract contextual information from the perspective of categories, and a channel attention module is embedded in it to achieve multiple contextual information extraction. Combined with the remote sensing atrous spatial pyramid pooling module (RSASPP), which is composed of atrous convolution with different expansion rates, feature fusion of objects at different scales is achieved. The refinement module (RM) is added for boundary refinement to achieve finer segmentation. Experiments are conducted on the WHDLD dataset to prove the effectiveness of the method.