Abstract: Sparse representation has proved to be a powerful tool in hyperspectral image (HSI) classification, and the advantages of joint classification using multifeature information have also attracted in HSI classification field. However, the sparse strategy of multifeature data and the non-linearity in data are two difficult problems. A kernel adaptive sparse model is proposed to classify hyperspectral images. For several complementary features (gradient, texture and shape), the proposed model simultaneously obtains the representation vector for each feature, and utilizes the adaptive sparse strategy ladaptive,0 to effectively use the multifeature information. The adaptive sparse strategy not only limits the representation of pixels in different feature spaces by atoms from a particular class, but also allows the selected atoms of these pixels to be different, thus providing a better representation. In addition, the proposed kernel joint sparse representation model is used to deal with non-linear problems of data. The kernel model projects data into high-dimensional space to improve separability and achieve better performance than linear models. The experimental results of the Indian Pines and University of Pavia show that the proposed algorithm exhibits a higher classification accuracy.

Key words: hyperspectral image classification, joint sparse representation, feature extraction, kernel