Multi-objective multi-verse optimizer based unsupervised band selection for hyperspectral image classification-Reference-Cited by-同舟云学术

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Multi-objective multi-verse optimizer based unsupervised band selection for hyperspectral image classification

Published:2022-06-03 Issue:11 Volume:43 Page:3990-4024
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Sawant Shrutika S.¹,Prabukumar Manoharan²,Loganathan Agilandeeswari²,Alenizi Farhan A.³,Ingaleshwar Subodh⁴

Affiliation:

1. Multimodal Human Sensing, Fraunhofer-Institut für Integrierte Schaltungen IIS Am Wolfsmantel, Erlangen, Germany

2. School of Information Technology & Engineering, Vellore Institute of Technology, Vellore, India

3. Electrical Engineering Department, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

4. Department of E & IE, JSSATE, Bengaluru, india

Funder

New Faculty Grant Program, PSAU – College of Engineering, Saudi Arabia

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2022.2105666

Reference63 articles.

1. A binary multi-verse optimizer for 0-1 multidimensional knapsack problems with application in interactive multimedia systems

2. Hyperspectral image analysis. A tutorial

3. Hyperspectral Remote Sensing Data Analysis and Future Challenges

4. Survey on classification methods for hyper spectral remote sensing imagery

5. Hyperspectral image classification using fuzzy-embedded hyperbolic sigmoid nonlinear principal component and weighted least squares approach

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2. Adaptive feature selection for hyperspectral image classification based on Improved Unsupervised Mayfly optimization Algorithm;Earth Science Informatics;2024-06-28

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4. Quantitative analysis of the hexamethylenetetramine concentration in a hexamethylenetetramine–acetic acid solution using near infrared spectroscopy: A comprehensive study on preprocessing methods and variable selection techniques;Journal of Near Infrared Spectroscopy;2024-03-25

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