Open Access

ARTICLE

An Intelligent Deep Learning Based Xception Model for Hyperspectral Image Analysis and Classification

by J. Banumathi¹, A. Muthumari², S. Dhanasekaran³, S. Rajasekaran⁴, Irina V. Pustokhina⁵, Denis A. Pustokhin⁶, K. Shankar^7,*

1 Department of Information Technology, University College of Engineering, Nagercoil, 629004, India
2 Department of Computer Science and Engineering, University College of Engineering, Ramanathapuram, 623513, India
3 Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, 626128, India
4 Department of Electrical Engineering, PSN College of Engineering, Tirunelveli, 627152, India
5 Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow, 117997, Russia
6 Department of Logistics, State University of Management, Moscow, 109542, Russia
7 Department of Computer Applications, Alagappa University, Karaikudi, 630001, India

* Corresponding Author: K. Shankar. Email:

Computers, Materials & Continua 2021, 67(2), 2393-2407. https://doi.org/10.32604/cmc.2021.015605

Received 30 November 2020; Accepted 25 December 2020; Issue published 05 February 2021

Abstract

Due to the advancements in remote sensing technologies, the generation of hyperspectral imagery (HSI) gets significantly increased. Accurate classification of HSI becomes a critical process in the domain of hyperspectral data analysis. The massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground materials. Presently, deep learning (DL) models particularly, convolutional neural networks (CNNs) become useful for HSI classification owing to the effective feature representation and high performance. In this view, this paper introduces a new DL based Xception model for HSI analysis and classification, called Xcep-HSIC model. Initially, the presented model utilizes a feature relation map learning (FRML) to identify the relationship among the hyperspectral features and explore many features for improved classifier results. Next, the DL based Xception model is applied as a feature extractor to derive a useful set of features from the FRML map. In addition, kernel extreme learning machine (KELM) optimized by quantum-behaved particle swarm optimization (QPSO) is employed as a classification model, to identify the different set of class labels. An extensive set of simulations takes place on two benchmarks HSI dataset, namely Indian Pines and Pavia University dataset. The obtained results ensured the effective performance of the Xcep-HSIC technique over the existing methods by attaining a maximum accuracy of 94.32% and 92.67% on the applied India Pines and Pavia University dataset respectively.

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Keywords

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