A Parallel Computing Technique for Complete Modular Eigenspace Feature Extraction of Hyperspectral Images

In this paper, we present a parallel computing technique for the feature extraction of hyperspectral images. The approach is based on the complete modular eigenspace (CME) scheme, which was designed to extract the simplest and most efficient feature modules by a newly defined multi-dimensional correlation matrix to optimize the modular eigenspace for high- dimensional datasets. The CME feature extraction scheme improves the performance of feature extraction by modifying the correlation coefficient operations. The proposed parallel CME (PCME) scheme is introduced to reduce the computational load of CME feature extraction using the parallel computing technique. It is implemented by parallel virtual machine (PVM) to solve the huge matrix problems of CME feature extraction. The performance of the proposed method is evaluated by applying to hyperspectral images of MODIS/ASTER (MASTER) airborne simulator during the Pacrim II project. The experiments demonstrate the proposed PCME approach is an effective scheme not only for the feature extraction but also for the band selection of high-dimensional datasets. It can improve the precision of hyperspectral image classification compared to conventional multispectral classification schemes.