Exploiting Spatial Context in Nonlinear Mapping of Hyperspectral Image Data

Abstract

Hyperspectral remote sensing image analysis is a challenging task due to the nature of such images. Therefore, dimensionality reduction techniques are often used as a step prior to image analysis. Although there are approaches, which exploit spatial information in image analysis, there is a lack of papers devoted to the problem of exploiting spatial information in dimensionality reduction methods. This paper is devoted to the problem of exploiting spatial context in nonlinear mapping method, which is one of the oldest and well-known dimensionality reduction techniques. To address this task, we use two possible approaches, based on window functions, and order statistics. We provide experimental results for several tasks of hyperspectral image analysis, namely classification, segmentation, and visualization. All the experiments were conducted using well-known hyperspectral images.