Hyperspectral Image Classification via Inverse Mahalanobis Attention Network

Due to its powerful feature extraction and representation capabilities, deep learning has been successfully applied in the field of hyperspectral image classification. In patchbased hyperspectral image classification, where the central pixel represents the true category, extracting spectral features similar to the central pixel spectrum is crucial. To this end, we develop a new framework, called the inverse Mahalanobis attention network (IMAN), to address the spectral similarity feature extraction problem. The proposed framework develops a self-attention mechanism module based on the Mahalanobis distance to better learn the correlation between feature vectors of pixels, thereby efficiently suppressing noise generated by different land cover categories within patches. The feature extraction capability is further enhanced by integrating a dual-stream network structure that separates spatial and spectral information in hyperspectral images. Experiments conducted on on real hyperspectral datasets demonstrate the effectiveness and superiority of the proposed method compared to several state-of-the-art hyperspectral image classification methods.