Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification-Reference-Cited by-同舟云学术

Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification

Published:2020-11-10 Issue:1 Volume:42 Page:286-321
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Uddin Md. Palash¹²^ORCID,Mamun Md. Al³^ORCID,Afjal Masud Ibn²^ORCID,Hossain Md. Ali³

Affiliation:

1. School of IT, Deakin University, Geelong, Australia

2. Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh

3. Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2020.1807650

Reference87 articles.

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3. Baumgartner, M. F., L. Labial, and D. A. Landgrebe. 2015. “220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992 Indian Pine Test Site 3.” Purdue University Research Repository.

4. Reducing the Dimensionality of Plant Spectral Databases

5. Multiple Classifier Systems in Remote Sensing: From Basics to Recent Developments

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