Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data

Remote Sensing

Hsuan Ren¹  Yung-Ling Wang²  Yang-Lang Chang³  Min-Yu Huang³  Hung-Ming Kao⁴ 

[1] Center for Space and Remote Sensing Research, National Central University, No. 300,Jhong-da Rd., Jhong-li City 320, Taiwan;Department of Computer Science and Information Engineering, National Central University, No. 300, Jung-da Rd., Chung-li City 320, Taiwan;Department of Electrical Engineering, National Taipei University of Technology, No. 1, Sec. 3, Chung-Hsiao E. Rd., Taipei City 106, Taiwan;Institute of Applied Geosciences, National Taiwan Ocean University, No. 2, Pei-Ning Rd.,Keelung 202, Taiwan;

关键词: hyperspectral;    remote sensing;    ensemble empirical mode decomposition (EEMD);    spectral angle mapper;    similarity measurement;   

DOI  :  10.3390/rs6032069

来源: DOAJ

【 摘 要 】

This study proposed a new approach to measure the similarity between spectra to discriminate materials and evaluate the performance of parameter-selection procedures. Many pure pixel vector-based similarity measurements have been developed in the past to calculate the distance between two pixel vectors. However, those methods may not be effective since they do not take full advantage of the spectral correlation. In this study, we adopt Ensemble Empirical Mode Decomposition (EEMD) to decompose the spectrum into serial components and employ these components to improve the performance of spectral discrimination. Performance evaluation was conducted with several commonly used measurements, and the spectral samples used for experimentation were provided by the spectral library of United States Geological Survey (USGS). The experimental results have demonstrated that EEMD can extract the spectral features more effectively than common spectral similarity measurements, and it better characterizes spectral properties. Our experimental results also suggest general rules for selecting noise standard deviation, the number of iterations for EEMD and the collection of Intrinsic Mode Functions (IMFs) for classification. Finally, since EEMD is a time-consuming algorithm, we also implement parallel processing with a Graphics Processing Unit (GPU) to increase the processing speed.

【 授权许可】

Unknown