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 as an unsupervised approach are popularly used to solve this problem and are widely used in unsupervised binary change detection tasks. However, the existing self-supervised methods in change detection are based on pre-tasks or at patch-level, which may be sub-optimal for pixel-wise change detection tasks. Therefore, in this work, a pixel-wise contrastive approach is proposed to overcome this limitation. This is achieved by using contrastive loss in pixel-level features on an unlabeled multi-view setting. In this approach, a Siamese ResUnet is trained to obtain pixel-wise representations and to align features from shifted positive pairs. Meanwhile, vector quantization is used to augment the learned features in two branches. The final binary change map is obtained by subtracting features of one branch from features of the other branch and using the Rosin thresholding method. To overcome the effects of regular seasonal changes 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 multi-view contrastive method.

翻译：深层学习技术在遥感图像变化探测方面取得了巨大成功。 其中多数是监督技术,通常需要大量培训数据,并限于特定应用。 普遍使用自我监督方法作为不受监督的方法来解决这一问题,并在未经监督的二进制变化探测任务中广泛使用。 然而,现有的变化探测自监督方法是基于预先任务或补丁级的,这可能是对像素变化探测任务的亚最佳程度。 因此,在这项工作中,提出了克服这一限制的多级对比方法。 这是通过在未贴标签的多视图设置中使用像素级特征中的对比性损失来实现的。 在这种方法中,亚马斯- ResUnet 接受培训, 以获得像素表达方式和对正对配的特征进行匹配。 同时, 矢量的分解可以用来增强两个分支的学习特征。 最终的二进制变化图是通过将一个分支的特性从另一个分支的特性中减去,并利用罗斯- CD 临界值 方法, 来显示一个基数级化的比值, 并用一个基调方法来克服我们提出的数据稳定度变化的定期方法。, 使用一种比级方法,我们提出的数据变化的周期性变化的方法, 。 使用一种平流方法 使用一种稳定的比 。