Hard Region Aware Network for Remote Sensing Change Detection

TL;DR

This paper introduces HRANet, a change detection network that effectively identifies hard regions like boundaries and pseudo changes in remote sensing images by using hard region mining and multi-level feature aggregation.

Contribution

The paper proposes a novel HRANet with an online hard region estimation branch and cross-layer knowledge review to improve change detection accuracy in challenging regions.

Findings

HRANet outperforms existing methods on benchmark datasets.

Hard region mining improves detection accuracy in boundary areas.

Multi-level feature aggregation enhances change map quality.

Abstract

Change detection (CD) is essential for various real-world applications, such as urban management and disaster assessment. Numerous CD methods have been proposed, and considerable results have been achieved recently. However, detecting changes in hard regions, i.e., the change boundary and irrelevant pseudo changes caused by background clutters, remains difficult for these methods, since they pose equal attention for all regions in bi-temporal images. This paper proposes a novel change detection network, termed as HRANet, which provides accurate change maps via hard region mining. Specifically, an online hard region estimation branch is constructed to model the pixel-wise hard samples, supervised by the error between predicted change maps and corresponding ground truth during the training process. A cross-layer knowledge review module is introduced to distill temporal change information from low-level to high-level features, thereby enhancing the feature representation capabilities. Finally, the hard region aware features extracted from the online hard region estimation branch and multi-level temporal difference features are aggregated into a unified feature representation to improve the accuracy of CD. Experimental results on two benchmark datasets demonstrate the superior performance of HRANet in the CD task.