  Hyperspectral anomalous change detection has been a challenging task for its
emphasis on the dynamics of small and rare objects against the prevalent
changes. In this paper, we have proposed a Multi-Temporal spatial-spectral
Comparison Network for hyperspectral anomalous change detection (MTC-NET). The
whole model is a deep siamese network, aiming at learning the prevalent
spectral difference resulting from the complex imaging conditions from the
hyperspectral images by contrastive learning. A three-dimensional spatial
spectral attention module is designed to effectively extract the spatial
semantic information and the key spectral differences. Then the gaps between
the multi-temporal features are minimized, boosting the alignment of the
semantic and spectral features and the suppression of the multi-temporal
background spectral difference. The experiments on the "Viareggio 2013"
datasets demonstrate the effectiveness of proposed MTC-NET.
