A Survey of Graph and Attention Based Hyperspectral Image Classification
  Methods for Remote Sensing Data
Aryan Vats Manan Suri
The use of Deep Learning techniques for classification in Hyperspectral
Imaging (HSI) is rapidly growing and achieving improved performances. Due to
the nature of the data captured by sensors that produce HSI images, a common
issue is the dimensionality of the bands that may or may not contribute to the
label class distinction. Due to the widespread nature of class labels,
Principal Component Analysis is a common method used for reducing the
dimensionality. However,there may exist methods that incorporate all bands of
the Hyperspectral image with the help of the Attention mechanism. Furthermore,
to yield better spectral spatial feature extraction, recent methods have also
explored the usage of Graph Convolution Networks and their unique ability to
use node features in prediction, which is akin to the pixel spectral makeup. In
this survey we present a comprehensive summary of Graph based and Attention
based methods to perform Hyperspectral Image Classification for remote sensing
and aerial HSI images. We also summarize relevant datasets on which these
techniques have been evaluated and benchmark the processing techniques.