Adaptive Local Structure Consistency based Heterogeneous Remote Sensing Change Detection

TL;DR

This paper proposes an unsupervised change detection method for heterogeneous remote sensing images using adaptive local structure consistency, effectively addressing challenges posed by different sensor imaging mechanisms.

Contribution

It introduces a novel ALSC-based approach that constructs and projects adaptive graphs to measure structural changes without data leakage, enhancing change detection accuracy.

Findings

Outperforms state-of-the-art methods in experiments

Effectively detects changes across heterogeneous sensors

Maintains data privacy by local domain processing

Abstract

Change detection of heterogeneous remote sensing images is an important and challenging topic in remote sensing for emergency situation resulting from nature disaster. Due to the different imaging mechanisms of heterogeneous sensors, it is difficult to directly compare the images. To address this challenge, we explore an unsupervised change detection method based on adaptive local structure consistency (ALSC) between heterogeneous images in this letter, which constructs an adaptive graph representing the local structure for each patch in one image domain and then projects this graph to the other image domain to measure the change level. This local structure consistency exploits the fact that the heterogeneous images share the same structure information for the same ground object, which is imaging modality-invariant. To avoid the leakage of heterogeneous data, the pixelwise change image is calculated in the same image domain by graph projection. Experiment results demonstrate the effectiveness of the proposed ALSC based change detection method by comparing with some state-of-the-art methods.