Self-supervised Change Detection in Multi-view Remote Sensing Images

TL;DR

This paper introduces a self-supervised learning method for change detection in multi-view remote sensing images, effectively leveraging unlabeled data to improve detection accuracy and bridge the gap with supervised methods.

Contribution

The paper proposes a novel self-supervised change detection approach using multi-view contrastive learning and a pseudo-Siamese network, addressing feature diversity issues in unsupervised methods.

Findings

Outperforms existing unsupervised change detection methods.

Narrowing the performance gap between unsupervised and supervised approaches.

Effective on multiple heterogeneous remote sensing datasets.

Abstract

The vast amount of unlabeled multi-temporal and multi-sensor remote sensing data acquired by the many Earth Observation satellites present a challenge for change detection. Recently, many generative model-based methods have been proposed for remote sensing image change detection on such unlabeled data. However, the high diversities in the learned features weaken the discrimination of the relevant change indicators in unsupervised change detection tasks. Moreover, these methods lack research on massive archived images. In this work, a self-supervised change detection approach based on an unlabeled multi-view setting is proposed to overcome this limitation. This is achieved by the use of a multi-view contrastive loss and an implicit contrastive strategy in the feature alignment between multi-view images. In this approach, a pseudo-Siamese network is trained to regress the output between its two branches pre-trained in a contrastive way on a large dataset of multi-temporal homogeneous or heterogeneous image patches. Finally, the feature distance between the outputs of the two branches is used to define a change measure, which can be analyzed by thresholding to get the final binary change map. Experiments are carried out on five homogeneous and heterogeneous remote sensing image datasets. The proposed SSL approach is compared with other supervised and unsupervised state-of-the-art change detection methods. Results demonstrate both improvements over state-of-the-art unsupervised methods and that the proposed SSL approach narrows the gap between unsupervised and supervised change detection.