LLE-SVM Classification of Apple Mealiness Based on Hyperspectral Scattering Image

作者：Zhao Gui lin; Zhu Qi bing^*; Huang Min

来源：Spectroscopy and Spectral Analysis, 2010, 30(10): 2739-2743.

DOI：10.3964/j.issn.1000-0593(2010)10-2739-05

• Mealiness
• Hyperspectral scattering image
• Locally linear embedding
• Non-linear dimensionality reduction
• Support vector machine

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.

• 出版日期2010-10
• 单位江南大学