Self-Supervised Multisensor Change Detection

TL;DR

This paper introduces a self-supervised learning method for change detection between optical and SAR images, enabling effective analysis without labeled data, which is crucial for real-world scenarios like natural disasters.

Contribution

It presents a novel self-supervised approach combining deep clustering and contrastive learning for multi-sensor change detection using only unlabeled bi-temporal images.

Findings

Effective on four multi-modal scenes with change detection.

Outperforms traditional methods constrained by lack of labeled data.

Demonstrates robustness across different sensor modalities.

Abstract

Most change detection methods assume that pre-change and post-change images are acquired by the same sensor. However, in many real-life scenarios, e.g., natural disaster, it is more practical to use the latest available images before and after the occurrence of incidence, which may be acquired using different sensors. In particular, we are interested in the combination of the images acquired by optical and Synthetic Aperture Radar (SAR) sensors. SAR images appear vastly different from the optical images even when capturing the same scene. Adding to this, change detection methods are often constrained to use only target image-pair, no labeled data, and no additional unlabeled data. Such constraints limit the scope of traditional supervised machine learning and unsupervised generative approaches for multi-sensor change detection. Recent rapid development of self-supervised learning methods has shown that some of them can even work with only few images. Motivated by this, in this work we propose a method for multi-sensor change detection using only the unlabeled target bi-temporal images that are used for training a network in self-supervised fashion by using deep clustering and contrastive learning. The proposed method is evaluated on four multi-modal bi-temporal scenes showing change and the benefits of our self-supervised approach are demonstrated.