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.
翻译:超光谱异常变化探测是一项艰巨的任务,因为它强调小型和稀有物体相对于普遍变化的动态作用。在本文件中,我们提议建立一个用于超光谱异常变化探测的多时空空间光谱比较网络(MTC-NET),整个模型是一个深沙网,目的是通过对比性学习了解超光谱图像的复杂成像条件所产生的光谱差异。设计了一个三维空间光谱关注模块,以有效提取空间语义信息和关键光谱差异。然后将多时地特征之间的差距缩小到最低程度,促进语义和光谱特征的对齐,抑制多时地背景光谱差异。关于“Viareggio 2013”数据集的实验显示了拟议的MTC-NET的有效性。