Dual-Path Feature Aware Network for Remote Sensing Image Semantic Segmentation

Semantic segmentation is a significant task for remote sensing interpretation, which takes advantage of contextual semantic information to classify each pixel into a specific category. Most current methods apply convolutional neural networks (CNN) to learn feature representation from remote sensing images, which may ignore the global dependencies due to the limitation of convolutional kernels. Inspired by the global feature learning ability of Transformer, we propose a novel deep model called dual-path feature aware network (DPFANet), which combines the structure of CNN and Transformer for semantic segmentation of remote sensing images. DPFANet aims to learn effective modeling ability from local to global features of images. Simultaneously, an adaptive feature fusion network is developed to fuse features from dual-path networks. Moreover, an edge optimization block is applied to constrain the edge features, whose purpose is to obtain more representative features for segmentation. Experimental results on three public remote sensing datasets verify that our proposed network yields better segmentation performance compared to other related methods.