论文标题
高光谱异常检测的多阶段空间传播比较网络
Multi-Temporal Spatial-Spectral Comparison Network for Hyperspectral Anomalous Change Detection
论文作者
论文摘要
高光谱的异常变化检测是一项艰巨的任务，因为它强调针对普遍变化的小小的和稀有物体的动态。在本文中，我们提出了高光谱异常变化检测（MTC-NET）的多阶段空间谱比较网络。整个模型是一个深厚的暹罗网络，旨在通过对比度学习从高光谱图像中学习复杂的成像条件产生的普遍光谱差异。三维空间光谱注意模块旨在有效提取空间语义信息和关键频谱差异。然后，将多时间特征之间的差距最小化，从而增强了语义和光谱特征的比对，并抑制了多个颞背景频谱差异。 “ ViaReggio 2013”数据集上的实验证明了提出的MTC-NET的有效性。
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.
