SRC-Net: Bi-Temporal Spatial Relationship Concerned Network for Change Detection

• URL: http://arxiv.org/abs/2406.05668v2
• Date: Thu, 27 Jun 2024 14:55:41 GMT
• Title: SRC-Net: Bi-Temporal Spatial Relationship Concerned Network for Change Detection
• Authors: Hongjia Chen, Xin Xu, Fangling Pu,
• Abstract summary: Change detection (CD) in remote sensing imagery is a crucial task with applications in environmental monitoring, urban development, and disaster management. We propose SRC-Net: a bi-temporal spatial relationship concerned network for CD.
• Score: 9.682463974799893
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Change detection (CD) in remote sensing imagery is a crucial task with applications in environmental monitoring, urban development, and disaster management. CD involves utilizing bi-temporal images to identify changes over time. The bi-temporal spatial relationships between features at the same location at different times play a key role in this process. However, existing change detection networks often do not fully leverage these spatial relationships during bi-temporal feature extraction and fusion. In this work, we propose SRC-Net: a bi-temporal spatial relationship concerned network for CD. The proposed SRC-Net includes a Perception and Interaction Module that incorporates spatial relationships and establishes a cross-branch perception mechanism to enhance the precision and robustness of feature extraction. Additionally, a Patch-Mode joint Feature Fusion Module is introduced to address information loss in current methods. It considers different change modes and concerns about spatial relationships, resulting in more expressive fusion features. Furthermore, we construct a novel network using these two relationship concerned modules and conducted experiments on the LEVIR-CD and WHU Building datasets. The experimental results demonstrate that our network outperforms state-of-the-art (SOTA) methods while maintaining a modest parameter count. We believe our approach sets a new paradigm for change detection and will inspire further advancements in the field. The code and models are publicly available at https://github.com/Chnja/SRCNet.

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