Unlike the existing change detection approaches based on the multispectral (MS) image and synthetic aperture radar (SAR) image datasets, a novel unsupervised hyperspectral change detection (UHCD) framework is proposed in this paper. The UHCD framework is designed for hyperspectral images with high dimensions and low availability. This framework consists of two modules: the spectral mapping with adversarial learning and the discriminant analysis with spatial attribute optimization. In comparison with other advanced change detection methods, the proposed framework possesses three distinctive properties: (1) The unsupervised spectral mapping is leveraged to exploit underlying spectral features without the requirement of pseudo-training datasets in the change detection task; (2) We introduce spectral constraint loss into reconstruction space and adversarial loss into latent space to enhance the quality of the features extracted by the spectral mapping network; (3) Spatial attribute optimization uses the spatial correlation to further improve the performance of the proposed UHCD method. The experimental results on two real datasets show that the proposed UHCD achieves competitive performance.

与现有的基于多光谱 (MS) 图像和合成孔径雷达 (SAR) 图像数据集的变化检测方法不同，本文提出了一种新的无监督高光谱变化检测 (UHCD) 框架。UHCD 框架专为高维度和低可用性的高光谱图像而设计。该框架由两个模块组成：具有对抗性学习的光谱映射和具有空间属性优化的判别分析。与其他先进的变化检测方法相比，所提出的框架具有三个独特的特性：（1）利用无监督光谱映射来利用潜在的光谱特征，而无需在变化检测任务中使用伪训练数据集；(2) 我们在重构空间中引入谱约束损失，在潜在空间中引入对抗性损失，以提高谱映射网络提取的特征的质量；(3)空间属性优化利用空间相关性进一步提高了所提出的UHCD方法的性能。在两个真实数据集上的实验结果表明，所提出的 UHCD 实现了具有竞争力的性能。