Edge-Guided Parallel Network for VHR Remote Sensing Image Change Detection.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.(2023)
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.
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Key words
Change detection (CD),convolutional neural networks (CNNs),difference features,edge-guided network,remote sensing,two-stream architecture
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