Gabor feature based support vector guided dictionary learning for hyperspectral image classification

Discriminative dictionary learning aims to learn a dictionary from training samples in order to improve the discriminative ability of their coding vectors. Gabor wavelets have recently been successfully applied for hyperspectral image (HSI) classification due to their ability to extract joint spatial and spectrum information. Due to the high discriminative power of Gabor features, an efficient method, called Gabor feature based Support Vector Guided Dictionary Learning (GSVGDL), has been proposed in this paper for HSI classification. After Gabor features have been extracted from the hyperspectral image, the augmented Gabor feature matrix is used to construct the initial dictionary. The dictionary learning model formulates the discrimination term as the weighted summation of the squared distances between all pairs of coding vectors, which can greatly improve the discriminative ability of the dictionary. The structure of the dictionary and the corresponding linear classifier are obtained simultaneously by dictionary learning. Experimental results on two real hyperspectral image data have shown that the proposed GSVGDL approach could achieve better performance than several state-of-the-art methods.