Classification of Hyperspectral Images with Different Methods of Training Set Formation-Reference-Cited by-同舟云学术

Classification of Hyperspectral Images with Different Methods of Training Set Formation

Published:2018-01 Issue:1 Volume:54 Page:76-82
ISSN:8756-6990
Container-title:Optoelectronics, Instrumentation and Data Processing
language:en
Short-container-title:Optoelectron.Instrument.Proc.

Author:

Borzov S. M.,Potaturkin O. I.

Publisher

Allerton Press

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Link

http://link.springer.com/content/pdf/10.3103/S8756699018010120.pdf

Reference26 articles.

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2. T. H. Chan, A. Ambikapathi, W. K. Ma, and C. Y. Chi, “Robust Affine Set Fitting and Fast Simplex Volume Max-Min for Hyperspectral Endmember Extraction,” IEEE Trans. Geosci. Remote Sensing 51 (7), 3982–3997 (2013).

3. K. Cawse-Nicholson, S. B. Damelin, A. Robin, and M. Sears, “Determining the Intrinsic Dimension of a Hyperspectral Image Using Random Matrix Theory,” IEEE Trans. Image Process. 22 (4), 1301–1310 (2013).

4. S. M. Borzov and O. I. Potaturkin, “Classification of Vegetation Types on the Basis of Remote Sensing,” Vestn. NGU, Ser. Inform. Tekhnol. 12 (4), 13–22 (2014).

5. O. I. Potaturkin, S. M. Borzov, A. O. Potaturkin, and S. B. Uzilov, “Methods and Technologies of Processing of Multispectral and Hyperspectral Data of High-Resolution Remote Sensing,” Vych. Tekhnol. 18 (special issue), 53–60 (2013).

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