HCANet: A Hierarchical Context Aggregation Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Abstract

Many practical applications of high-resolution remote sensing images (HRRSIs) are based on semantic segmentation. However, due to the complex ground object information contained in remote sensing images, it is difficult to make precise semantic segmentation of HRRSIs. In this letter, we proposed a hierarchical context aggregation network (HCANet) for the semantic segmentation of HRRSIs. The HCANet has an encoder-decoder structure which is similar to UNet. In the HCANet, we designed two Compact Atrous Spatial Pyramid Pooling (CASPP and CASPP+) modules. The CASPP modules replace the copy and crop operation in UNet to extract the multiscale context information of the multisemantic features of ResNet. The CASPP+ module is embedded in the middle layer of HCANet’s decoder to provide a strong aggregation path of contextual information. In the decoder of HCANet, the multiscale context information obtained by CASPP modules is hierarchically merged layer by layer for the semantic segmentation of HRRSIs. We compared our method with several of the most advanced methods on the ISPRS Vaihingen and Potsdam data sets. The final results demonstrate that our method can achieve outstanding performance.