Binary Change Guided Hyperspectral Multiclass Change Detection

TL;DR

This paper introduces BCG-Net, an unsupervised deep learning framework that enhances hyperspectral multiclass change detection by leveraging binary change detection guidance, temporal correlation constraints, and iterative optimization to improve accuracy and spectral unmixing.

Contribution

The paper proposes a novel BCG-Net that integrates binary change detection guidance with spectral unmixing and temporal constraints for improved hyperspectral multiclass change detection.

Findings

BCG-Net achieves superior multiclass change detection performance.

The method improves spectral unmixing accuracy.

Iterative optimization reduces error accumulation.

Abstract

Characterized by tremendous spectral information, hyperspectral image is able to detect subtle changes and discriminate various change classes for change detection. The recent research works dominated by hyperspectral binary change detection, however, cannot provide fine change classes information. And most methods incorporating spectral unmixing for hyperspectral multiclass change detection (HMCD), yet suffer from the neglection of temporal correlation and error accumulation. In this study, we proposed an unsupervised Binary Change Guided hyperspectral multiclass change detection Network (BCG-Net) for HMCD, which aims at boosting the multiclass change detection result and unmixing result with the mature binary change detection approaches. In BCG-Net, a novel partial-siamese united-unmixing module is designed for multi-temporal spectral unmixing, and a groundbreaking temporal correlation constraint directed by the pseudo-labels of binary change detection result is developed to guide the unmixing process from the perspective of change detection, encouraging the abundance of the unchanged pixels more coherent and that of the changed pixels more accurate. Moreover, an innovative binary change detection rule is put forward to deal with the problem that traditional rule is susceptible to numerical values. The iterative optimization of the spectral unmixing process and the change detection process is proposed to eliminate the accumulated errors and bias from unmixing result to change detection result. The experimental results demonstrate that our proposed BCG-Net could achieve comparative or even outstanding performance of multiclass change detection among the state-of-the-art approaches and gain better spectral unmixing results at the same time.