**AttentionȬBased Spatial and Spectral Network with PCAȬ Guided SelfȬSupervised Feature Extraction for Changeȱ ȱ Detection in Hyperspectral Images**

**Zhao Wang 1, Fenlong Jiang 1, Tongfei Liu 1, Fei Xie 2,\* and Peng Li <sup>1</sup>**


This paper proposed an attention-based spatial and spectral network with a PCAguided, self-supervised feature extraction mechanism to detect changes in hyperspectral images. It consists of two steps: a self-supervised mapping from each patch of the difference map to the principal components of the central pixel of each patch with spatial features of differences extracted by a multilayer convolutional neural network in the first step, followed by an attention mechanism which calculates adaptive weights between spatial and spectral features of each pixel from concatenated spatial and spectral features in the second step. Finally, a joint analysis of the weighted spatial and spectral features was used to detect the changes of pixels in different positions. Experimental results on several real hyperspectral change detection data sets showed the effectiveness and advancement of the proposed method.

remotesensingȬ12Ȭ02783Ȭv2
