[LLE-SVM classification of apple mealiness based on hyperspectral scattering image].
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.
School of Communication and Control Engineering, Jiangnan University, Wuxi 214122, China. lin861103@sina.com
Gui-lin
GL
Zhao
Qi-bing
QB
Zhu
Min
M
Huang
D005504:Food Analysis / Q000379:methods*; D027845:Malus; D013057:Spectrum Analysis; D060388:Support Vector Machine