  Hyperspectral image (HSI) classification is the most vibrant area of research
in the hyperspectral community due to the rich spectral information contained
in HSI can greatly aid in identifying objects of interest. However, inherent
non-linearity between materials and the corresponding spectral profiles brings
two major challenges in HSI classification: interclass similarity and
intraclass variability. Many advanced deep learning methods have attempted to
address these issues from the perspective of a region/patch-based approach,
instead of a pixel-based alternate. However, the patch-based approaches
hypothesize that neighborhood pixels of a target pixel in a fixed spatial
window belong to the same class. And this assumption is not always true. To
address 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 require
regions or patches centered around a raw target pixel to perform 2D-CNN based
classification, instead, our approach transforms 1D pixel vector in HSI into 2D
angular feature space using Gramian Angular Field (GAF) and then embed it to a
new neighborhood attention network to suppress irrelevant angular feature while
emphasizing on pertinent features useful for HSI classification task.
Evaluation results on three publicly available HSI datasets demonstrate the
superior performance of the proposed model.
