Semantic Segmentation of High-Resolution Remote Sensing Images Using Multiscale Skip Connection Network

Abstract

Semantic segmentation of remote sensing images plays a vital role in land resource management, yield estimation, and economic evaluation. Therefore, this paper proposes a multi-scale skip connection network with the Atrous convolution to deal with the segmentation problems of the multi-modal and multi-scale high-resolution remote sensing images. Firstly, we applied the Atrous convolution in the encoder to enlarge the convolution kernel’s receptive field. Secondly, based on the U-Net network, we merged the light and deep features of different scales by redesigning the skip connection and combining multi-scale features in each U-Net layer. Finally, we applied a pixel-by-pixel classification method and obtained the semantic segmentation results of remote sensing images. The effectiveness of the proposed algorithm is verified. The experimental results show that the mF1 scores are 89.4% and 90.3% on the open dataset of ISPRS Vaihingen and ISPRS Potsdam, respectively, which are better than the state-of-the-art algorithms.