摘要：

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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