Effective subspace detection based on the measurement of both the spectral and spatial information for hyperspectral image classification-Reference-Cited by-同舟云学术

Effective subspace detection based on the measurement of both the spectral and spatial information for hyperspectral image classification

Published:2020-07-16 Issue:19 Volume:41 Page:7541-7564
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Mishu Sadia Zaman¹,Ahmed Boshir¹,Hossain Md. Ali¹,Uddin Md. Palash²³^ORCID

Affiliation:

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

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

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

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2020.1763502

Reference46 articles.

1. 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.

2. An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection

3. A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification

4. Selection of optimum bands from TM scenes through mutual information analysis

5. Feature extraction of hyperspectral image using principal component analysis and folded-principal component analysis

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