GREEDY ALGORITHMS FOR PURE PIXELS IDENTIFICATION IN HYPERSPECTRAL UNMIXING: A MULTIPLE-MEASUREMENT VECTOR VIEWPOINT
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員

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摘要This paper studies a multiple-measurement vector (MMV)-based sparse regression approach to blind hyperspectral unmixing. In general, sparse regression requires a dictionary. The considered approach uses the measured hyperspectral data as the dictionary, thereby intending to represent the whole measured data using the fewest number of measured hyperspectral vectors. We tackle this self-dictionary MMV (SD-MMV) approach using greedy pursuit. It is shown that the resulting greedy algorithms are identical or very similar to some representative pure pixels identification algorithms, such as vertex component analysis. Hence, our study provides a new dimension on understanding and interpreting pure pixels identification methods. We also prove that in the noiseless case, the greedy SD-MMV algorithms guarantee perfect identification of pure pixels when the pure pixel assumption holds.
著者Fu X, Ma WK, Chan TH, Bioucas-Dias JM, Iordache MD
會議名稱21st European Signal Processing Conference (EUSIPCO)
會議開始日09.09.2013
會議完結日13.09.2013
會議地點Marrakesh
會議國家/地區摩洛哥
詳細描述organized by European Signal Processing (EURASIP) Society,
出版年份2013
月份1
日期1
出版社IEEE
電子國際標準書號*****************
語言英式英語
Web of Science 學科類別Engineering; Engineering, Electrical & Electronic

上次更新時間 2021-19-02 於 23:55