GPU Parallel Implementation of Isometric Mapping for Hyperspectral Classification

Manifold learning algorithms such as the isometric mapping (ISOMAP) algorithm have been widely used in the analysis of hyperspectral images (HSIs), for both visualization and dimension reduction. As advanced versions of the traditional linear projection techniques, the manifold learning algorithms find the low-dimensional feature representation by nonlinear mapping, which can better preserve the local structure of the original data and thus benefit the data analysis. However, the high computational complexity of the manifold learning algorithms hinders their application in HSI processing. Although there are a few parallel implementations of manifold learning approaches that are available in the remote sensing community, they have not been designed to accelerate the eigen-decomposition process, which is actually the most time-consuming part of the manifold learning algorithms. In this letter, as a case study, we discuss the graphics processing unit parallel implementation of the ISOMAP algorithm. In particular, we focus on the eigen-decomposition process and verify the applicability of the proposed method by validating the embedding vectors and the subsequent classification accuracies. The experimental results obtained on different HSI data sets show an excellent speedup performance and consistent classification accuracy compared with the serial implementation.