  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.
