@Article{cmes.2022.020601,
AUTHOR = {Somenath Bera, Vimal K. Shrivastava, Suresh Chandra Satapathy},
TITLE = {Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {133},
YEAR = {2022},
NUMBER = {2},
PAGES = {219--250},
URL = {http://www.techscience.com/CMES/v133n2/48965},
ISSN = {1526-1506},
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 by this
successful performance, this paper presents a systematic review of different CNN architectures for HSI classification
and provides some future guidelines. To accomplish this, our study has taken a few important steps. First, we have
focused on different CNN architectures, which are able to extract spectral, spatial, and joint spectral-spatial features.
Then, many publications related to CNN based HSI classifications have been reviewed systematically. Further, a
detailed comparative performance analysis has been presented between four CNN models namely 1D CNN, 2D
CNN, 3D CNN, and feature fusion based CNN (FFCNN). Four benchmark HSI datasets have been used in our
experiment for evaluating the performance. Finally, we concluded the paper with challenges on CNN based HSI
classification and future guidelines that may help the researchers to work on HSI classification using CNN.},
DOI = {10.32604/cmes.2022.020601}
}