Task-related self-supervised learning for remote sensing image change detection

• URL: http://arxiv.org/abs/2105.04951v1
• Date: Tue, 11 May 2021 11:44:04 GMT
• Title: Task-related self-supervised learning for remote sensing image change detection
• Authors: Zhinan Cai, Zhiyu Jiang, Yuan Yuan
• Abstract summary: Change detection for remote sensing images is widely applied for urban change detection, disaster assessment and other fields. Most of the existing CNN-based change detection methods still suffer from the problem of inadequate pseudo-changes suppression and insufficient feature representation. In this work, an unsupervised change detection method based on Task-related Self-supervised Learning Change Detection network with smooth mechanism is proposed to eliminate it.
• Score: 8.831857715361624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Change detection for remote sensing images is widely applied for urban change detection, disaster assessment and other fields. However, most of the existing CNN-based change detection methods still suffer from the problem of inadequate pseudo-changes suppression and insufficient feature representation. In this work, an unsupervised change detection method based on Task-related Self-supervised Learning Change Detection network with smooth mechanism(TSLCD) is proposed to eliminate it. The main contributions include: (1) the task-related self-supervised learning module is introduced to extract spatial features more effectively. (2) a hard-sample-mining loss function is applied to pay more attention to the hard-to-classify samples. (3) a smooth mechanism is utilized to remove some of pseudo-changes and noise. Experiments on four remote sensing change detection datasets reveal that the proposed TSLCD method achieves the state-of-the-art for change detection task.

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