Orthogonal polynomial function fitting for hyperspectral data representation and discrimination-Reference-Cited by-同舟云学术

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Orthogonal polynomial function fitting for hyperspectral data representation and discrimination

Published:2016-11 Issue: Volume:83 Page:160-168
ISSN:0167-8655
Container-title:Pattern Recognition Letters
language:en
Short-container-title:Pattern Recognition Letters

Author:

Li Liwei,Zhang Bing,Li Wei,Gao Lianru

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference24 articles.

1. Hyperspectral remote sensing data analysis and future challenges;Bioucas-Dias;IEEE Geosci. Remote Sens. Mag.,2013

2. Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction;Bruce;IEEE Trans. Geosci. Remote Sens.,2002

3. Wavelets for computationally efficient hyperspectral derivative analysis;Bruce;IEEE Trans. Geosci. Remote Sens.,2001

4. An adaptive similarity measure for classification of hyperspectral signatures;Bue;IEEE Geosci. Remote Sens. Lett.,2013

5. Choosing multiple parameters for support vector machines;Chapelle;Mach. Learn.,2002

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Special issue on advances in pattern recognition in remote sensing;Pattern Recognition Letters;2016-11

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