Edge-Guided Parallel Network for VHR Remote Sensing Image Change Detection

Abstract

Change Detection (CD) is an important research topic in the remote sensing field, and it has a wide range of applications, including resource monitoring, disaster assessment, urban planning, etc. Recently, Deep Learning (DL) has shown its advantages in CD. However, most existing DL-based methods can not capture the complementary information between bi-temporal and difference features. This paper proposes an Edge-Guided Parallel Network (EGPNet) to solve this problem. First, our EGPNet extracts bi-temporal and difference features simultaneously through a parallel encoding framework. During parallel encoding, we design an Supplementary Mechanism (SM) to enrich the difference features with bi-temporal features. Second, we fuse bi-temporal and difference features at each feature level to sufficiently exploit their complementarity. Finally, the Edge-Aware Module (EAM) and Edge-Guidance Feature Module (EFM) are introduced to enhance the edge representation for improving blurred edges of detection results. Benefiting from the rich change-related information in difference features and detailed information in bi-temporal features, our EGPNet can detect change regions entirely and accurately. Experimental results on the LEVIR-CD, SYSU-CD and CDD datasets demonstrate that the proposed method outperforms several state-of-the-art approaches. Especially our EGPNet can detect more precise and sharper edges than other methods. We open source the Pytorch implementation and the pre-trained model at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Lvkyky/EGPNet</uri>