Multi-source collaborative enhanced for remote sensing images semantic segmentation

Abstract

Remote sensing images semantic segmentation is a difficult instance of image understanding. Due to the regional variability and uncertainty of real-world ground cover features, the semantic segmentation of remote sensing images becomes a challenging task. In this paper, we propose an end-to-end multi-source remote sensing image semantic segmentation network (MCENet) aiming at the problems of intra-class inconsistency and inter-class indistinguishability in remote sensing images. Firstly, we design a collaborative enhanced fusion module to mine complementary characteristics of multi-source remote sensing images. Among them, the collaborative fusion module is used to solve the problem of intra-class difference, and the enhanced aggregation module is used to solve the problem of inter-class similarity. Secondly, a multi-scale decoder is proposed to improve the robustness of the model for small targets and large-scale changes by learning scale invariance features. Experimental results show that our method achieved 2.2% and 1.11% mean intersection over union (mIoU) score improvements compared with other methods on the US3D and ISPRS Potsdam data sets, respectively. In addition, the method proposed in this paper also has strong competitiveness in terms of parameter quantity and inference speed.