Dimensionality reduction method for hyperspectral image analysis based on rough set theory-Reference-Cited by-同舟云学术

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Dimensionality reduction method for hyperspectral image analysis based on rough set theory

Published:2020-01-01 Issue:1 Volume:53 Page:192-200
ISSN:2279-7254
Container-title:European Journal of Remote Sensing
language:en
Short-container-title:European Journal of Remote Sensing

Author:

Wang Zhenhua¹,Liang Suling¹^ORCID,Xu Lizhi¹,Song Wei¹,Wang Dexing¹,Huang Dongmei¹

Affiliation:

1. College of Information, Shanghai Ocean University , Shanghai, China

Funder

National Natural Science Foundation of China

Capacity Development for Local College Project

Publisher

Informa UK Limited

Subject

Applied Mathematics,Atmospheric Science,Computers in Earth Sciences,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/22797254.2020.1785949

Reference28 articles.

1. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review

2. Hyperspectral Remote Sensing Data Analysis and Future Challenges

3. Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality

4. Binary particle swarm optimisation and rough set theory for dimension reduction in classification

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1. An adaptive pyramid PSO for high-dimensional feature selection;Expert Systems with Applications;2024-12

2. Enhancing Hyperspectral Image Classification for Land Use Land Cover With Dilated Neighborhood Attention Transformer and Crow Search Optimization;IEEE Access;2024

3. A New Feature Selection Algorithm Based on Adversarial Learning for Solving Classification Problems;Communications in Computer and Information Science;2024

4. Features reduction collaborative fuzzy clustering for hyperspectral remote sensing images analysis;Journal of Intelligent & Fuzzy Systems;2023-11-04

5. A Rough Set Approach to Dimensionality Reduction for Performance Enhancement in Machine Learning;International Journal of Emerging Scientific Research;2022-10-31

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