| 光谱学与光谱分析 |
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| LLE-SVM Classification of Apple Mealiness Based on Hyperspectral Scattering Image |
| ZHAO Gui-lin, ZHU Qi-bing*, HUANG Min |
| School of Communication and Control Engineering, Jiangnan University, Wuxi 214122, China |
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Abstract Apple mealiness degree is an important factor for its internal quality. hyperspectral scattering, as a promising technique, was investigated for noninvasive measurement of apple mealiness. In the present paper, a locally linear embedding (LLE) coupled with support vector machine (SVM) was proposed to achieve classification because of large number of image data. LLE is a nonlinear lowering dimension method, which reveals the structure of the global nonlinearity by the local linear joint. This method can effectively calculate high-dimensional input data embedded in a low-dimensional space manifold. The dimension reduction of hyperspectral data was classified by SVM. Comparing the LLE-SVM classification method with the traditional SVM classification, the results indicated that the training accuracy obtained with the LLE-SVM was higher than that just with SVM; and the testing accuracy of the classifier changed a little before and after dimensionality reduction, and the range of fluctuation was less than 5%. It is expected that LLE-SVM method would provide an effective classification method for apple mealiness nondestructive detection using hyperspectral scattering image technique.
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Received: 2009-11-26
Accepted: 2010-03-02
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Corresponding Authors:
ZHU Qi-bing
E-mail: zhuqib@163.com
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