Multi-Temporal Spatial-Spectral Comparison Network for Hyperspectral Anomalous Change Detection

• URL: http://arxiv.org/abs/2205.11395v1
• Date: Mon, 23 May 2022 15:41:27 GMT
• Title: Multi-Temporal Spatial-Spectral Comparison Network for Hyperspectral Anomalous Change Detection
• Authors: Meiqi Hu, Chen Wu, Bo Du
• Abstract summary: 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. The experiments on the "Viareggio 2013" datasets demonstrate the effectiveness of proposed MTC-NET.
• Score: 32.23764287942984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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