Open Access

ARTICLE

Fusion-Based Deep Learning Model for Hyperspectral Images Classification

Kriti¹, Mohd Anul Haq², Urvashi Garg¹, Mohd Abdul Rahim Khan^2,*, V. Rajinikanth³

1 Department of Computer Science and Engineering, Chandigarh University, Mohali, 140413, India
2 Department of Computer Science, College of Computer Science and Information Science, Majmaah University, AL-Majmaah, 11952, Saudi Arabia
3 Department of Electronics and Instrumentation Engineering, St. Joseph's College of Engineering, Chennai, 600119, Tamil Nadu, India

* Corresponding Author: Mohd Abdul Rahim Khan. Email:

(This article belongs to the Special Issue: Applications of Intelligent Systems in Computer Vision)

Computers, Materials & Continua 2022, 72(1), 939-957. https://doi.org/10.32604/cmc.2022.023169

Received 30 August 2021; Accepted 09 December 2021; Issue published 24 February 2022

Abstract

A crucial task in hyperspectral image (HSI) taxonomy is exploring effective methodologies to effusively practice the 3-D and spectral data delivered by the statistics cube. For classification of images, 3-D data is adjudged in the phases of pre-cataloging, an assortment of a sample, classifiers, post-cataloging, and accurateness estimation. Lastly, a viewpoint on imminent examination directions for proceeding 3-D and spectral approaches is untaken. In topical years, sparse representation is acknowledged as a dominant classification tool to effectually labels deviating difficulties and extensively exploited in several imagery dispensation errands. Encouraged by those efficacious solicitations, sparse representation (SR) has likewise been presented to categorize HSI's and validated virtuous enactment. This research paper offers an overview of the literature on the classification of HSI technology and its applications. This assessment is centered on a methodical review of SR and support vector machine (SVM) grounded HSI taxonomy works and equates numerous approaches for this matter. We form an outline that splits the equivalent mechanisms into spectral aspects of systems, and spectral–spatial feature networks to methodically analyze the contemporary accomplishments in HSI taxonomy. Furthermore, cogitating the datum that accessible training illustrations in the remote distinguishing arena are generally appropriate restricted besides training neural networks (NNs) to necessitate an enormous integer of illustrations, we comprise certain approaches to increase taxonomy enactment, which can deliver certain strategies for imminent learnings on this issue. Lastly, numerous illustrative neural learning-centered taxonomy approaches are piloted on physical HSI's in our experimentations.

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Keywords

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