Volume 9 Number 6 (Jun. 2014)
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JSW 2014 Vol.9(6): 1532-1537 ISSN: 1796-217X
doi: 10.4304/jsw.9.6.1532-1537

Plant Species Identification Based on Independent Component Analysis for Hyperspectral Data

Yachao Wang1, Gang Wu1, Lixia Ding2
1Information School, Beijing Forestry University, Beijing 100083, China
2School of Environmental & Resource Sciences, Zhejiang Agriculture & Forestry University, Linan 311300, Zhejiang Province, China


Abstract—By investigating the possibility of plant species classification based on independent component analysis (ICA) for hyperspectral data with minor difference, the framework of a general plant species classification model that consists of ICA based data reduction, classifier training and verification is proposed in this paper. Five different types of discriminant analysis classifiers including Linear, Quadratic, DiagLinear, DiagQuatic and Mahalanobis, with data reduction that based on principal components analysis (PCA) and ICA, are implemented and compared. Accuracy assessment of classification for real leaf hyperspectral data is demonstrated, indicating that data reduction based on ICA performs better than that of PCA. Moreover, the proposed classification model with ICA based data reduction and Quadratic Discriminant Analysis works best, and its accuracy is about 98.35% with dimension 25 reduced from 2500.

Index Terms—independent component analysis, data reduction, principal component analysis, supervision classification, hyperspectral data analysis

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Cite: Yachao Wang, Gang Wu, Lixia Ding, "Plant Species Identification Based on Independent Component Analysis for Hyperspectral Data," Journal of Software vol. 9, no. 6, pp. 1532-1537, 2014.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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