HYPERSPECTRAL IMAGE FEATURE EXTRACTION BASED ON GENERALIZED DISCRIMINANT ANALYSIS

摘要

The hyperspectral image enriches spectrum information, so compared with panchromatic image and multispectral image; it can classify the ground target better. The feature extraction of hyperspectral image is the necessary step of the ground target classification, and the kernel method is a new way to extract the nonlinear feature. In this paper, First the mathematical model of the generalized discriminant analysis was described, and then the processing method of this model was given, finally, we did two experiments. Through the tests, we can see that, in the feature space extracted by generalized discriminant analysis, the samples of the same class are near with each other; the samples of the different classes are far away. It can be concluded that the method described in this paper is suitable to hyperspectral image classification, and it can do better job than the method of linear discriminant analysis.