  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.
