TL;DR
SRC-Net is a novel change detection network that effectively leverages bi-temporal spatial relationships through specialized modules, improving accuracy and robustness in remote sensing imagery analysis.
Contribution
The paper introduces SRC-Net with a Perception and Interaction Module and a Patch-Mode joint Feature Fusion Module, enhancing bi-temporal feature extraction and fusion by focusing on spatial relationships.
Findings
Outperforms state-of-the-art methods on LEVIR-CD and WHU datasets.
Maintains a modest parameter count while improving detection accuracy.
Demonstrates the effectiveness of spatial relationship modeling in change detection.
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…
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Taxonomy
MethodsConvNeXt
