Somenath Bera1, Vimal K. Shrivastava2, Suresh Chandra Satapathy3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 219-250, 2022, DOI:10.32604/cmes.2022.020601
- 21 July 2022
Abstract Hyperspectral image (HSI) classification has been one of the most important tasks in the remote sensing community
over the last few decades. Due to the presence of highly correlated bands and limited training samples in HSI,
discriminative feature extraction was challenging for traditional machine learning methods. Recently, deep learning
based methods have been recognized as powerful feature extraction tool and have drawn a significant amount of
attention in HSI classification. Among various deep learning models, convolutional neural networks (CNNs) have
shown huge success and offered great potential to yield high performance in HSI classification. Motivated… More >
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