A dual-kernel spectral-spatial classification approach for hyperspectral images based on Mahalanobis distance metric learning

Hyperspectral images provide a precise representation of the earth’s surface, with abundant spectral and spatial features, but normal classification algorithms use only the information provided by the spectral features of each data point. In this paper, we propose a new approach to hyperspectral image classification based on Mahalanobis distance metric learning and kernel learning that considers both the features of the spectral bands and a spatial prior. This approach consists of two components. First, we obtain a primary labeled classification result and a posterior probability distribution for each pixel point using a Mahalanobis-kernel-based classifier. Second, instead of the original or extracted spectral features, we reconstruct the spatial relationship of the hyperspectral images using the posterior probability of every data point, smooth the boundaries, and revise suspicious points based on this piecewise information using a kernel-based multi-region segmentation method. In an experimental study, we adopt a support vector machine (SVM) classifier as the kernel classifier to obtain the posterior probabilities using dimensionally reduced data. The proposed method is compared with several other methods from various perspectives. Simulation experiments run on several real hyperspectral data sets are reported. The results show that the proposed method performs better than other comparable classification algorithms, especially in a condition-constrained environment.