Foundation Model-Driven Semantic Change Detection in Remote Sensing Imagery

• URL: http://arxiv.org/abs/2602.13780v1
• Date: Sat, 14 Feb 2026 13:56:31 GMT
• Title: Foundation Model-Driven Semantic Change Detection in Remote Sensing Imagery
• Authors: Hengtong Shen, Li Yan, Hong Xie, Yaxuan Wei, Xinhao Li, Wenfei Shen, Peixian Lv, Fei Tan,
• Abstract summary: We propose PerASCD, a semantic change detection (SCD) method driven by RS foundation model PerA.<n>We introduce a modular Cascaded Gated Decoder (CG-Decoder) that simplifies complex SCD decoding pipelines.<n>Our decoder achieves state-of-the-art (SOTA) performance on two public benchmark datasets.
• Score: 12.711361119734542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote sensing (RS) change detection methods can extract critical information on surface dynamics and are an essential means for humans to understand changes in the earth's surface and environment. Among these methods, semantic change detection (SCD) can more effectively interpret the multi-class information contained in bi-temporal RS imagery, providing semantic-level predictions that support dynamic change monitoring. However, due to the limited semantic understanding capability of the model and the inherent complexity of the SCD tasks, existing SCD methods face significant challenges in both performance and paradigm complexity. In this paper, we propose PerASCD, a SCD method driven by RS foundation model PerA, designed to enhance the multi-scale semantic understanding and overall performance. We introduce a modular Cascaded Gated Decoder (CG-Decoder) that simplifies complex SCD decoding pipelines while promoting effective multi-level feature interaction and fusion. In addition, we propose a Soft Semantic Consistency Loss (SSCLoss) to mitigate the numerical instability commonly encountered during SCD training. We further explore the applicability of multiple existing RS foundation models on the SCD task when equipped with the proposed decoder. Experimental results demonstrate that our decoder not only effectively simplifies the paradigm of SCD, but also achieves seamless adaptation across various vision encoders. Our method achieves state-of-the-art (SOTA) performance on two public benchmark datasets, validating its effectiveness. The code is available at https://github.com/SathShen/PerASCD.git.

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