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

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.