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The use of Deep Learning techniques for classification in HyperspectralImaging (HSI) is rapidly growing and achieving improved performances. Due tothe nature of the data captured by sensors that produce HSI images, a commonissue is the dimensionality of the bands that may or may not contribute to thelabel class distinction. Due to the widespread nature of class labels,Principal Component Analysis is a common method used for reducing thedimensionality. However,there may exist methods that incorporate all bands ofthe Hyperspectral image with the help of the Attention mechanism. Furthermore,to yield better spectral spatial feature extraction, recent methods have alsoexplored the usage of Graph Convolution Networks and their unique ability touse node features in prediction, which is akin to the pixel spectral makeup. Inthis survey we present a comprehensive summary of Graph based and Attentionbased methods to perform Hyperspectral Image Classification for remote sensingand aerial HSI images. We also summarize relevant datasets on which thesetechniques have been evaluated and benchmark the processing techniques.

A Survey of Graph and Attention Based Hyperspectral Image Classification Methods for Remote Sensing Data