Deep-learning techniques have achieved great success in remote-sensing image change detection. Most of them are supervised techniques, which usually require large amounts of training data and are limited to a particular application. Self-supervised methods solve these problems and are widely used in unsupervised binary change detection tasks. However, the existing self-supervised methods in change detection are suboptimal for pixel-wise change detection tasks. In this work, a pixel-wise contrastive approach is proposed to overcome this limitation. This is achieved by using contrastive loss in superpixel-level features on an unlabeled multiview setting. In this approach, a pseudo-Siamese network is trained to obtain pixel-wise representations and to align features from shifted image pairs. The final binary change map is obtained by using thresholding methods on learned temporal features. To overcome the season-related noise in binary change maps, we also used an uncertainty method to enhance the temporal robustness of the proposed approach. Two homogeneous (OSCD and MUDS) datasets and one heterogeneous (California Flood) dataset are used to evaluate the performance of the proposed approach. Results demonstrate improvements in both efficiency and accuracy over the patch-wise multiview contrastive method.

中文翻译：

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双时间 RS 图像中像素级变化检测的自监督方法

深度学习技术在遥感图像变化检测方面取得了巨大成功。它们中的大多数是有监督的技术，通常需要大量的训练数据并且仅限于特定的应用程序。自监督方法解决了这些问题，并广泛用于无监督二进制变化检测任务。然而，现有的变化检测中的自监督方法对于逐像素变化检测任务来说并不理想。在这项工作中，提出了一种像素级对比方法来克服这一限制。这是通过在未标记的多视图设置上使用超像素级特征的对比损失来实现的。在这种方法中，训练一个伪连体网络以获得逐像素表示并对齐来自移位图像对的特征。最终的二进制变化图是通过对学习的时间特征使用阈值方法获得的。为了克服二进制变化图中与季节相关的噪声，我们还使用了一种不确定性方法来增强所提出方法的时间鲁棒性。使用两个同质（OSCD 和 MUDS）数据集和一个异构（加利福尼亚洪水）数据集来评估所提出方法的性能。结果表明，与补丁多视图对比方法相比，效率和准确性都有所提高。使用两个同质（OSCD 和 MUDS）数据集和一个异构（加利福尼亚洪水）数据集来评估所提出方法的性能。结果表明，与补丁多视图对比方法相比，效率和准确性都有所提高。使用两个同质（OSCD 和 MUDS）数据集和一个异构（加利福尼亚洪水）数据集来评估所提出方法的性能。结果表明，与补丁多视图对比方法相比，效率和准确性都有所提高。