Multiple Classifier Ensembles with Band Clustering for Hyperspectral Image Classification-Reference-Cited by-同舟云学术

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Multiple Classifier Ensembles with Band Clustering for Hyperspectral Image Classification

Published:2014-01 Issue:1 Volume:47 Page:217-227
ISSN:2279-7254
Container-title:European Journal of Remote Sensing
language:en
Short-container-title:European Journal of Remote Sensing

Author:

Su Hongjun¹²,Du Peijun¹

Affiliation:

1. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, 163 Xianlin Road, 210046 - Nanjing, China

2. School of Earth Sciences and Engineering, Hohai University, 1 Xikang Road, 210098 - Nanjing, China

Publisher

Informa UK Limited

Subject

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

Link

https://www.tandfonline.com/doi/pdf/10.5721/EuJRS20144714

Reference12 articles.

1. A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data

2. Chang C-I. (2013)—Hyperspectral Data Processing: Algorithm Design and Analysis. New Jersey: Wiley-Interscience, 1–5.

3. Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis

4. On the mean accuracy of statistical pattern recognizers

5. Best-bases feature extraction algorithms for classification of hyperspectral data

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1. Multiple classifier system for remotely sensed data clustering;IET Image Processing;2021-10-04

2. Spectral-spatial hyperspectral image classification based on superpixel and multi-classifier fusion;International Journal of Remote Sensing;2020-06-01

3. Band clustering using expectation–maximization algorithm and weighted average fusion-based feature extraction for hyperspectral image classification;Journal of Applied Remote Sensing;2018-11-02

4. Sparse Representation Classification Based on Flexible Patches Sampling of Superpixels for Hyperspectral Images;Mathematical Problems in Engineering;2018-10-02

5. Anomaly Detection from Hyperspectral Images Using Clustering Based Feature Reduction;Journal of the Indian Society of Remote Sensing;2018-06-22

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