Parallel and adaptive reduction of hyperspectral data to intrinsic dimensionality

Abstract

Recent advances in sensor technology have led to the development of hyperspectral sensors capable of collecting remote sensing imagery at several hundred bands over the spectrum. While these developments hold great promise for Earth science, they create new processing challenges. Therefore, processing hyperspectral data using new efficient techniques is a must.One such approach of effectively processing hyperspectral data is through dimension reduction prior to the use of data in applications such as land use/land cover classifications. Principal Component Analysis (PCA) is perhaps the most popular dimension reduction technique. The outcome is a number of principal components (PCs) in a descending order of information content. It is often the case that only a small number of these components contain the effective information needed. In this paper, we propose an adaptive parallel technique for determining the effective dimensionality of hyperspectral data on computer clusters. Based on a user-specified desired level of information content, the method selects adaptively the faster technique for solving the eigenproblem and computes only the needed components for that level of information.