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Hyperspectral image (HSI) classification is the most vibrant area of researchin the hyperspectral community due to the rich spectral information containedin HSI can greatly aid in identifying objects of interest. However, inherentnon-linearity between materials and the corresponding spectral profiles bringstwo major challenges in HSI classification: interclass similarity andintraclass variability. Many advanced deep learning methods have attempted toaddress these issues from the perspective of a region/patch-based approach,instead of a pixel-based alternate. However, the patch-based approacheshypothesize that neighborhood pixels of a target pixel in a fixed spatialwindow belong to the same class. And this assumption is not always true. Toaddress this problem, we herein propose a new deep learning architecture,namely Gramian Angular Field encoded Neighborhood Attention U-Net (GAF-NAU),for pixel-based HSI classification. The proposed method does not requireregions or patches centered around a raw target pixel to perform 2D-CNN basedclassification, instead, our approach transforms 1D pixel vector in HSI into 2Dangular feature space using Gramian Angular Field (GAF) and then embed it to anew neighborhood attention network to suppress irrelevant angular feature whileemphasizing on pertinent features useful for HSI classification task.Evaluation results on three publicly available HSI datasets demonstrate thesuperior performance of the proposed model.

GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification