Open Access

REVIEW

Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review

Somenath Bera¹, Vimal K. Shrivastava², Suresh Chandra Satapathy^3,*

1 School of Computer Science and Engineering, Lovely Professional University, Phagwara, 144411, India
2 School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, 751024, India
3 School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, 751024, India

* Corresponding Author: Suresh Chandra Satapathy. Email:

Computer Modeling in Engineering & Sciences 2022, 133(2), 219-250. https://doi.org/10.32604/cmes.2022.020601

Received 02 December 2021; Accepted 16 March 2022; Issue published 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 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.

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