| Summary: | 碩士 === 國立臺北科技大學 === 電機工程系研究所 === 95 === A novel study of feature extraction technique for hyperspectral images of remote sensing is proposed. The method is based on the greedy modular eigenspace (GME) scheme, which was designed to extract the simplest and the most efficient feature modules for high-dimensional datasets. It presents a framework which consists of two algorithms, referred to as multi-dimensional correlation matrix feature extraction (MD-CMFE) and the feature scale uniformity transformation (FSUT). The MD-CMFE scheme, also known as the complete modular eigenspace (CME), can improve the performance of GME feature extraction optimally by modifying the conventional correlation coefficient operations. It is designed to extract features by a new defined three dimensional correlation matrix (3D-CM) to optimize the modular eigenspace, while FSUT is performed to fuse most correlated features from different spectrums associated with different data sources. In this paper, we also present a parallel computing technique for the feature extraction of hyperspectral images. 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 MD-CMFE/FSUT and 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.
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