Spatially Selective Self-Training for Unsupervised Building Change Detection

• URL: http://arxiv.org/abs/2606.10775v2
• Date: Wed, 10 Jun 2026 05:38:22 GMT
• Title: Spatially Selective Self-Training for Unsupervised Building Change Detection
• Abstract summary: Unsupervised building change detection aims to learn building-change masks from unlabeled bi-temporal remote sensing images.<n>SST-CD is a spatially selective self-training framework that reformulates fully label-free building change detection as end-to-end detector learning.<n>Experiments show that SST-CD achieves F1 scores of 83.08%, 91.69%, and 86.60%, respectively, outperforming existing unsupervised and label-free baselines.
• Score: 8.884818023657276
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Unsupervised building change detection aims to learn building-change masks from unlabeled bi-temporal remote sensing images. Existing label-free methods often follow a discrepancy-to-mask paradigm, directly using temporal differences, frozen foundation-model responses, prompt-based outputs, or post-processing results as final change maps. Although these strategies provide annotation-free cues, they do not learn a task-specific building-change detector and remain vulnerable to the gap between generic temporal discrepancies and building-defined structural changes. In practice, such discrepancies are often noisy and task-irrelevant, as appearance shifts, registration errors, and non-building modifications can produce strong but misleading responses. To address this problem, we propose SST-CD, a spatially selective self-training framework that reformulates fully label-free building change detection as end-to-end detector learning under noisy pseudo supervision. SST-CD uses temporal discrepancies as candidate pseudo labels and trains the detector only on spatially reliable pixels, whose reliability is estimated by a local consistency criterion that filters inconsistent regions from supervision. To further stabilize noisy self-training, a lightweight feature adapter recalibrates bi-temporal features, while a prototype-based decoder produces compact change and no-change representations. Experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show that SST-CD achieves F1 scores of 83.08%, 91.69%, and 86.60%, respectively, outperforming existing unsupervised and label-free baselines.

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