Hyperspectral Image Classification using Principal Component Analysis and Vision Transformer
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I1P157Keywords:
Hyperspectral Image Classification, Principal Component Analysis, Vision Transformer, Convolutional Neural NetworksAbstract
Hyperspectral image classification is essential in remote sensing applications, aiming to accurately categorize land cover or material depicted in hyperspectral data. This paper makes use of Vision transformer model with Principal component analysis as a dimensionality reduction technique. The Vision Transformer model utilizes self-attention mechanisms to capture spatial dependencies among image patches unlike traditional CNN layers. Experiments were conducted on standard hyperspectral datasets including Indian Pines, University of Pavia for training the model.
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