HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON INTELLIGENT OPTIMIZATION FEATURE SELECTION

Hyperspectral image classification has always been a hot topic. The problem of "dimension disaster" is caused by the high dimension of pixel points and the lack of labeled training sample points. In order to reduce the data dimension, an intelligent optimization algorithm was proposed for feature selection. The new method introduces the principle of mutual information and symmetric uncertainty, constructs the fitness function, selects the candidate feature set with the intelligent optimization algorithm, and obtains the optimal feature set. The SVM classifier was trained in the optimized feature set. In real hyperspectral data set, the new method was compared with various feature selection methods, and the experimental results showed that the optimal feature set has a high classification accuracy.