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 cannot capture the complementary information between bitemporal and difference features. This article proposes an edge-guided parallel network (EGPNet) to solve this problem. First, our EGPNet extracts bitemporal and difference features simultaneously through a parallel encoding framework. During parallel encoding, we design a supplementary mechanism to enrich the difference features with bitemporal features. Second, we fuse bitemporal and difference features at each feature level to sufficiently exploit their complementarity. Finally, the edge-aware module and edge-guidance feature module 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 bitemporal 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.

Keywords

• Change detection (CD)
• convolutional neural networks (CNNs)
• difference features
• edge-guided network
• remote sensing
• two-stream architecture