Spectral-Spatial Classification of Hyperspectral Images With a Superpixel-Based Discriminative Sparse Model

A novel superpixel-based discriminative sparse model (SBDSM) for spectral-spatial classification of hyperspectral images (HSIs) is proposed. Here, a superpixel in a HSI is considered as a small spatial region whose size and shape can be adaptively adjusted for different spatial structures. In the proposed approach, the SBDSM first clusters the HSI into many superpixels using an efficient oversegmentation method. Then, pixels within each superpixel are jointly represented by a set of common atoms from a dictionary via a joint sparse regularization. The recovered sparse coefficients are utilized to determine the class label of the superpixel. In addition, instead of directly using a large number of sampled pixels as dictionary atoms, the SBDSM applies a discriminative K-SVD learning algorithm to simultaneously train a compact representation dictionary, as well as a discriminative classifier. Furthermore, by utilizing the class label information of training pixels and dictionary atoms, a class-labeled orthogonal matching pursuit is proposed to accelerate the K-SVD algorithm while still enforcing high discriminability on sparse coefficients when training the classifier. Experimental results on four real HSI datasets demonstrate the superiority of the proposed SBDSM algorithm over several well-known classification approaches in terms of both classification accuracies and computational speed.