Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation

• URL: http://arxiv.org/abs/2504.14306v1
• Date: Sat, 19 Apr 2025 14:05:39 GMT
• Title: Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation
• Authors: Yitao Zhao, Sen Lei, Nanqing Liu, Heng-Chao Li, Turgay Celik, Qing Zhu,
• Abstract summary: We propose a self-supervision motivated CD framework with geometric estimation, called "MatchCD"<n>The proposed MatchCD framework utilizes the zero-shot capability to optimize the encoder with self-supervised contrastive representation.<n>Unlike the conventional change detection requiring segmenting the full-frame image into small patches, our MatchCD framework can directly process the original large-scale image.
• Score: 15.50183955507315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As an essential procedure in earth observation system, change detection (CD) aims to reveal the spatial-temporal evolution of the observation regions. A key prerequisite for existing change detection algorithms is aligned geo-references between multi-temporal images by fine-grained registration. However, in the majority of real-world scenarios, a prior manual registration is required between the original images, which significantly increases the complexity of the CD workflow. In this paper, we proposed a self-supervision motivated CD framework with geometric estimation, called "MatchCD". Specifically, the proposed MatchCD framework utilizes the zero-shot capability to optimize the encoder with self-supervised contrastive representation, which is reused in the downstream image registration and change detection to simultaneously handle the bi-temporal unalignment and object change issues. Moreover, unlike the conventional change detection requiring segmenting the full-frame image into small patches, our MatchCD framework can directly process the original large-scale image (e.g., 6K*4K resolutions) with promising performance. The performance in multiple complex scenarios with significant geometric distortion demonstrates the effectiveness of our proposed framework.

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