Discriminative Gabor Feature Selection for Hyperspectral Image Classification

Three-dimensional Gabor wavelets have recently been successfully applied for hyperspectral image classification due to their ability to extract joint spatial and spectrum information. However, the dimension of the extracted Gabor feature is incredibly huge. In this letter, we propose a symmetrical-uncertainty-based and Markov-blanket-based approach to select informative and nonredundant Gabor features for hyperspectral image classification. The extracted Gabor features with large dimension are first ranked by their information contained for classification and then added one by one after investigating the redundancy with already selected features. The proposed approach was fully tested on the widely used Indian Pine site data. The results show that the selected features are much more efficient and can achieve similar performance with previous approach using only hundreds of features.