Hyperspectral Image Classification with Deep CNN Using an Enhanced Elephant Herding Optimization for Updating Hyper-Parameters

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10254684" target="_blank" >RIV/61989100:27240/23:10254684 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2079-9292/12/5/1157" target="_blank" >https://www.mdpi.com/2079-9292/12/5/1157</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/electronics12051157" target="_blank" >10.3390/electronics12051157</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hyperspectral Image Classification with Deep CNN Using an Enhanced Elephant Herding Optimization for Updating Hyper-Parameters

Popis výsledku v původním jazyce

Deep learning approaches based on convolutional neural networks (CNNs) have recently achieved success in computer vision, demonstrating significant superiority in the domain of image processing. For hyperspectral image (HSI) classification, convolutional neural networks are an efficient option. Hyperspectral image classification approaches are often based on spectral information. Convolutional neural networks are used for image classification in order to achieve greater performance. The complex computation in convolutional neural networks requires hyper-parameters that attain high accuracy outputs, and this process needs more computational time and effort. Following up on the proposed technique, a bio-inspired metaheuristic strategy based on an enhanced form of elephant herding optimization is proposed in this research paper. It allows one to automatically search for and target the suitable values of convolutional neural network hyper-parameters. To design an automatic system for hyperspectral image classification, the enhanced elephant herding optimization (EEHO) with the AdaBound optimizer is implemented for the tuning and updating of the hyper-parameters of convolutional neural networks (CNN-EEHO-AdaBound). The validation of the convolutional network hyper-parameters should produce a highly accurate response of high-accuracy outputs in order to achieve high-level accuracy in HSI classification, and this process takes a significant amount of processing time. The experiments are carried out on benchmark datasets (Indian Pines and Salinas) for evaluation. The proposed methodology outperforms state-of-the-art methods in a performance comparative analysis, with the findings proving its effectiveness. The results show the improved accuracy of HSI classification by optimising and tuning the hyper-parameters.

Název v anglickém jazyce

Hyperspectral Image Classification with Deep CNN Using an Enhanced Elephant Herding Optimization for Updating Hyper-Parameters

Popis výsledku anglicky

Deep learning approaches based on convolutional neural networks (CNNs) have recently achieved success in computer vision, demonstrating significant superiority in the domain of image processing. For hyperspectral image (HSI) classification, convolutional neural networks are an efficient option. Hyperspectral image classification approaches are often based on spectral information. Convolutional neural networks are used for image classification in order to achieve greater performance. The complex computation in convolutional neural networks requires hyper-parameters that attain high accuracy outputs, and this process needs more computational time and effort. Following up on the proposed technique, a bio-inspired metaheuristic strategy based on an enhanced form of elephant herding optimization is proposed in this research paper. It allows one to automatically search for and target the suitable values of convolutional neural network hyper-parameters. To design an automatic system for hyperspectral image classification, the enhanced elephant herding optimization (EEHO) with the AdaBound optimizer is implemented for the tuning and updating of the hyper-parameters of convolutional neural networks (CNN-EEHO-AdaBound). The validation of the convolutional network hyper-parameters should produce a highly accurate response of high-accuracy outputs in order to achieve high-level accuracy in HSI classification, and this process takes a significant amount of processing time. The experiments are carried out on benchmark datasets (Indian Pines and Salinas) for evaluation. The proposed methodology outperforms state-of-the-art methods in a performance comparative analysis, with the findings proving its effectiveness. The results show the improved accuracy of HSI classification by optimising and tuning the hyper-parameters.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Electronics

ISSN

2079-9292

e-ISSN

2079-9292

Svazek periodika

12

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

18

Strana od-do

—

Kód UT WoS článku

000947607900001

EID výsledku v databázi Scopus

2-s2.0-85149939945