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.
翻译:遥感图像的变化探测被广泛用于城市变化探测、灾害评估和其他领域,但是,现有有线电视新闻网的改变探测方法大多仍受到伪变化抑制不足和特征代表不足等问题的影响;在这项工作中,提议采用基于与任务有关的自监督学习变化探测网络的未经监督的改变探测方法,并有平稳的机制(TSLCD),以消除这种变化;主要贡献包括:(1) 采用与任务有关的自我监督学习模块,以更有效地提取空间特征。 (2) 运用硬取样功能,更多地注意难以分类的样本。 (3) 利用一种顺利的机制消除一些假变化和噪音。