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

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

9 Jun 2024arXiv:2406.05668archive 2025-07-28

Hongjia Chen, Xin Xu, Fangling Pu

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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Code

Chnja/SRCNet officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Building change detection for remote sensing imagesChange Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Building change detection for remote sensing images LEVIR-CD SRC-Net F1 92.24 #4 of 37 Archive leaderboard report
Building change detection for remote sensing images LEVIR-CD SRC-Net IoU 85.60 #4 of 37 Archive leaderboard report
Building change detection for remote sensing images LEVIR-CD SRC-Net Params(M) 5.17 #4 of 37 Archive leaderboard report
Building change detection for remote sensing images WHU Building Dataset SRC-Net F1 92.06 #1 of 2 Archive leaderboard report
Building change detection for remote sensing images WHU Building Dataset SRC-Net IoU 85.28 #1 of 2 Archive leaderboard report
Building change detection for remote sensing images WHU Building Dataset SRC-Net Params(M) 5.17 #1 of 2 Archive leaderboard report
Change Detection LEVIR-CD SRC-Net F1 92.24 #8 of 28 Archive leaderboard report
Change Detection LEVIR-CD SRC-Net F1-score 92.24 #8 of 28 Archive leaderboard report
Change Detection LEVIR-CD SRC-Net IoU 85.60 #8 of 28 Archive leaderboard report
Change Detection WHU-CD SRC-Net F1 92.06 #13 of 22 Archive leaderboard report
Change Detection WHU-CD SRC-Net IoU 85.28 #13 of 22 Archive leaderboard report
Change Detection WHU-CD SRC-Net Overall Accuracy 99.30 #13 of 22 Archive leaderboard report
Change Detection WHU-CD SRC-Net Precision 92.57 #13 of 22 Archive leaderboard report
Change Detection WHU-CD SRC-Net Recall 91.55 #13 of 22 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

ConvNeXt

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