DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric Space

TL;DR

DeepCL introduces a novel deep learning framework that combines metric learning and segmentation to improve change detection in remote sensing images, addressing temporal modeling and pseudo-change issues for more accurate Earth surface monitoring.

Contribution

The paper proposes a deep change feature learning framework that integrates a contrastive loss with segmentation, enhancing temporal modeling and reducing false change detection in remote sensing.

Findings

DeepCL outperforms state-of-the-art methods in accuracy.

It demonstrates strong robustness against pseudo-changes.

The framework is adaptable to various change detection algorithms.

Abstract

Change detection (CD) is an important yet challenging task in the Earth observation field for monitoring Earth surface dynamics. The advent of deep learning techniques has recently propelled automatic CD into a technological revolution. Nevertheless, deep learning-based CD methods are still plagued by two primary issues: 1) insufficient temporal relationship modeling and 2) pseudo-change misclassification. To address these issues, we complement the strong temporal modeling ability of metric learning with the prominent fitting ability of segmentation and propose a deep change feature learning (DeepCL) framework for robust and explainable CD. Firstly, we designed a hard sample-aware contrastive loss, which reweights the importance of hard and simple samples. This loss allows for explicit modeling of the temporal correlation between bi-temporal remote sensing images. Furthermore, the modeled temporal relations are utilized as knowledge prior to guide the segmentation process for detecting change regions. The DeepCL framework is thoroughly evaluated both theoretically and experimentally, demonstrating its superior feature discriminability, resilience against pseudo changes, and adaptability to a variety of CD algorithms. Extensive comparative experiments substantiate the quantitative and qualitative superiority of DeepCL over state-of-the-art CD approaches.