J. Banumathi1, A. Muthumari2, S. Dhanasekaran3, S. Rajasekaran4, Irina V. Pustokhina5, Denis A. Pustokhin6, K. Shankar7,*
CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2393-2407, 2021, DOI:10.32604/cmc.2021.015605
- 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,… More >
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