Research Papers

Classification of hyperspectral remote sensing imagery by k-nearest-neighbor simplex based on adaptive C-mutual proportion standard deviation metric

[+] Author Affiliations
Shanjing Chen

State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei Anhui 230037, China

Logistical Engineering University of PLA, Chongqi 401331, China

Anhui Province Key Laboratory of Electronic Restriction, Hefei Anhui 230037, China

Yihua Hu

State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei Anhui 230037, China

Anhui Province Key Laboratory of Electronic Restriction, Hefei Anhui 230037, China

Shilong Xu

State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei Anhui 230037, China

Le Li

State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei Anhui 230037, China

Yizhe Cheng

State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei Anhui 230037, China

J. Appl. Remote Sens. 8(1), 083578 (Aug 05, 2014). doi:10.1117/1.JRS.8.083578
History: Received December 5, 2013; Revised April 24, 2014; Accepted June 27, 2014
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Abstract.  The k-nearest-neighbor simplex (kNNS) based on an adaptive C-mutual proportion standard deviation metric for classification of hyperspectral remote sensing imagery was proposed. By analyzing spectral characteristics on the samples of the same and different classes, a C-mutual proportion standard deviation metric is put forward which innovates a novel metric on distance and similarity measures for pattern recognition. Combined with the adaptive adjusting algorithm, this metric is used for the classification of hyperspectral remote sensing imagery. The traditional kNNS classification algorithm is improved by this metric and the adaptive adjusting algorithm, and its classification accuracy is enhanced. Three experiments with different types of hyperspectral imagery are conducted to evaluate the performance of the proposed algorithm in comparison to the other five classification algorithms. The experimental results demonstrate that the proposed algorithm is superior to other algorithms on overall accuracy and kappa coefficient.

© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Shanjing Chen ; Yihua Hu ; Shilong Xu ; Le Li and Yizhe Cheng
"Classification of hyperspectral remote sensing imagery by k-nearest-neighbor simplex based on adaptive C-mutual proportion standard deviation metric", J. Appl. Remote Sens. 8(1), 083578 (Aug 05, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083578


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