Testing the suitability of v-Support Vector Machine for hyperspectral image classification-Reference-Cited by-同舟云学术

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Testing the suitability of v-Support Vector Machine for hyperspectral image classification

Published:2024-05-06 Issue: Volume: Page:1-14
ISSN:1947-9832
Container-title:International Journal of Image and Data Fusion
language:en
Short-container-title:International Journal of Image and Data Fusion

Author:

Okwuashi Onuwa¹,Ndehedehe Christopher E.²³,Olayinka Dupe N.⁴,Akpomrere Rufus¹,Eyo Etim⁵,Ogbijara Ufuoma¹

Affiliation:

1. Department of Surveying and Geoinformatics, Delta State University of Science and Technology, Ozoro, Nigeria

2. Australian Rivers Institute, Griffith University, Nathan, QLD, Australia

3. School of Environment & Science, Griffith University, Nathan, QLD, Australia

4. Department of Surveying and Geoinformatics, University of Lagos, Lagos, Nigeria

5. Department of Surveying and Geoinformatics, Federal University of Science and Technology, Minna, Nigeria

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19479832.2024.2349999

Reference62 articles.

1. Feature Selection and Classification of Hyperspectral Images With Support Vector Machines

2. A hybrid convolution transformer for hyperspectral image classification

3. A Parameters Optimization Method of v-Support Vector Machine and Its Application in Speech Recognition

4. Optimization of One versus All-SVM using AdaBoost algorithm for rainfall classification and estimation from multispectral MSG data

5. Hyperspectral image classification based on three-dimensional adaptive sampling and improved iterative shrinkage-threshold algorithm

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