Hyperspectral Image Classification using Deep Learning Techniques: A Review

International Journal of Electronics and Communication Engineering

© 2022 by SSRG - IJECE Journal

Volume 9 Issue 6

Year of Publication : 2022

Authors : Syeda Sara Samreen, Hakeem Aejaz Aslam

10.14445/23488549/IJECE-V9I6P101

How to Cite?

Syeda Sara Samreen, Hakeem Aejaz Aslam, "Hyperspectral Image Classification using Deep Learning Techniques: A Review," SSRG International Journal of Electronics and Communication Engineering, vol. 9,  no. 6, pp. 1-4, 2022. Crossref, https://doi.org/10.14445/23488549/IJECE-V9I6P101

Abstract:

Hyperspectral image classification is a salient topic of research in the domain of remote sensing. The major problems faced during hyperspectral image classification are the curse of dimensionality and the availability of limited samples during training. It was originally developed for mining and geology purposes to identify hidden minerals. Hyperspectral Imaging has various applications in geosciences, agriculture, astrology, and surveillance. The advancement in computing technology has led to the development of significant deep-learning techniques that play an important role in successfully classifying remotely sensed data. This paper reviews the various deep-learning methods used for hyperspectral image classification. Then the research gaps and methodology for every paper have been highlighted. This paper aims to benefit and support other researchers in further research in this field.

Keywords:

Convolutional Neural Network, Deep Belief Network, Generative Adversarial Network, Recurrent Neural Network, Remote Sensing.

References:

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