Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36335
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: SF-ICNN: Spectral–Fractal Iterative Convolutional Neural Network for Classification of Hyperspectral Images
Author(s): Asghari Beirami, Behnam
Alizadeh Pirbasti, Mehran
Akbari, Vahid
Contact Email: vahid.akbari@stir.ac.uk
Keywords: iterative convolutional neural network
fractal features
hyperspectral image
spatial-spectral features
Issue Date: 21-Aug-2024
Date Deposited: 10-Oct-2024
Citation: Asghari Beirami B, Alizadeh Pirbasti M & Akbari V (2024) SF-ICNN: Spectral–Fractal Iterative Convolutional Neural Network for Classification of Hyperspectral Images. <i>Applied Sciences</i>, 14 (16), Art. No.: 7361. https://doi.org/10.3390/app14167361
Abstract: One primary concern in the field of remote-sensing image processing is the precise classification of hyperspectral images (HSIs). Lately, deep-learning models have demonstrated cutting-edge results in HSI classification. Despite this, researchers continue to study and propose simpler, more robust models. This study presents a novel deep-learning approach, the iterative convolutional neural network (ICNN), which combines spectral–fractal features and classifier probability maps iteratively, aiming to enhance the HSI classification accuracy. Experiments are conducted to prove the accuracy enhancement of the proposed method using HSI benchmark datasets of Indian pine (IP) and the University of Pavia (PU) to evaluate the performance of the proposed technique. The final results show that the proposed approach reaches overall accuracies of 99.16% and 95.5% on the IP and PU datasets, respectively, which are better than some basic methods. Additionally, the end findings demonstrate that greater accuracy levels might be achieved using a primary CNN network that employs the iteration loop than with certain current state-of-the-art spatial–spectral HSI classification techniques.
DOI Link: 10.3390/app14167361
Rights: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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