Prototype-oriented Unsupervised Change Detection for Disaster Management

TL;DR

This paper introduces PUCD, an unsupervised change detection method that effectively identifies disaster-related changes in satellite images without complex hyperparameter tuning, achieving state-of-the-art results.

Contribution

The paper presents a novel unsupervised change detection approach that combines prototype-oriented feature comparison with SAM refinement, reducing hyperparameter tuning complexity.

Findings

Achieves state-of-the-art performance on LEVIR-Extension dataset.

Does not require complex hyperparameter tuning.

Effective for disaster management applications.

Abstract

Climate change has led to an increased frequency of natural disasters such as floods and cyclones. This emphasizes the importance of effective disaster monitoring. In response, the remote sensing community has explored change detection methods. These methods are primarily categorized into supervised techniques, which yield precise results but come with high labeling costs, and unsupervised techniques, which eliminate the need for labeling but involve intricate hyperparameter tuning. To address these challenges, we propose a novel unsupervised change detection method named Prototype-oriented Unsupervised Change Detection for Disaster Management (PUCD). PUCD captures changes by comparing features from pre-event, post-event, and prototype-oriented change synthesis images via a foundational model, and refines results using the Segment Anything Model (SAM). Although PUCD is an unsupervised change detection, it does not require complex hyperparameter tuning. We evaluate PUCD framework on the LEVIR-Extension dataset and the disaster dataset and it achieves state-of-the-art performance compared to other methods on the LEVIR-Extension dataset.