Discriminative Eigenpixels-Based Dictionary Learning for Hyperspectral Image Classification

Sparse representation (SR) model has been applied to hyperspectral image (HSI) classification based on the observation that any spectral pixel could be approximately represented by a linear combination of several common pixels, but its discriminative ability is not deeply explored due to an insufficient description on spatial-spectral information and less emphasis on the dictionary structure. In this letter, we propose a new algorithm of HSI classification based on discriminative eigenpixels-based dictionary learning. Instead of using neighbor pixels directly, we define a new homogeneous region for each pixel, respectively, to exploit more spatial-spectral information and extract eigenpixels from homogeneous regions to preserve the essentials for each class. For the representation-based model, a discriminative eigenpixels-based dictionary is learned in homogeneous regions, where the locality of pixels is exploited to enhance the discriminative ability. Finally, we code a homogeneous region associated with a query pixel on the learned dictionary to determine its label by use of both the nearest neighbor (NN) classifier and majority voting. Experiments are conducted on four HSIs to demonstrate the effectiveness of the proposed method.