Consistency Change Detection Framework for Unsupervised Remote Sensing Change Detection

• URL: http://arxiv.org/abs/2511.08904v1
• Date: Thu, 13 Nov 2025 01:16:08 GMT
• Title: Consistency Change Detection Framework for Unsupervised Remote Sensing Change Detection
• Authors: Yating Liu, Yan Lu,
• Abstract summary: Unsupervised remote sensing change detection aims to monitor and analyze changes from multi-temporal remote sensing images in the same geometric region at different times.<n>Previous unsupervised methods attempt to achieve style transfer across multi-temporal remote sensing images through reconstruction by a generator network.<n>We propose a novel Consistency Change Detection Framework (CCDF) to address this challenge.
• Score: 25.281172690282258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised remote sensing change detection aims to monitor and analyze changes from multi-temporal remote sensing images in the same geometric region at different times, without the need for labeled training data. Previous unsupervised methods attempt to achieve style transfer across multi-temporal remote sensing images through reconstruction by a generator network, and then capture the unreconstructable areas as the changed regions. However, it often leads to poor performance due to generator overfitting. In this paper, we propose a novel Consistency Change Detection Framework (CCDF) to address this challenge. Specifically, we introduce a Cycle Consistency (CC) module to reduce the overfitting issues in the generator-based reconstruction. Additionally, we propose a Semantic Consistency (SC) module to enable detail reconstruction. Extensive experiments demonstrate that our method outperforms other state-of-the-art approaches.

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