Hyperspectral classification using a composite kernel driven by nearest-neighbor spatial features

Abstract

There is increasing interest in driving supervised classification of hyperspectral imagery by a support vector machine using a composite kernel employing both spectral and spatial features. While the spectral signature of the current hyper-spectral pixel is often used directly to supply the spectral feature, a statistic - such as the mean - calculated across a spatial window surrounding the pixel is typically employed as a spatial feature. In contrast, a nearest-neighbor spatial feature is proposed in which the nearest neighbors in Euclidean distance to the current pixel are used to calculate the spatial feature. It is argued that the proposed nearest-neighbor spatial feature is more likely to incorporate relevant, same-class neighbor pixels than window-based features for which borders between coherent single-class regions may give rise to misclassification. Experimental results illustrate the performance advantage of the proposed nearest-neighbor framework at supervised hyperspectral classification in comparison to several competing benchmark algorithms that also employ kernel-based support vector machines.