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Chinese Optics Letters

Chinese Optics Letters


  • Vol. 1, Iss. 11 — Nov. 20, 2003
  • pp: 637–640

Similarity measure of spectral vectors based on set theory and its application in hyperspectral RS image retrieval

Peijun Du, Tao Fang, Hong Tang, and Pengfei Shi  »View Author Affiliations

Chinese Optics Letters, Vol. 1, Issue 11, pp. 637-640 (2003)

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In this paper, two new similarity measure methods based on set theory were proposed. Firstly, similarity measure of two sets based on set theory and set operation was discussed. This principle was used to spectral vectors, and two approaches were proposed. The first method was to create a spectral polygon corresponding to spectral curve, and similarity of two spectral vectors can be replaced by that of two polygons. Area of spectral polygon was used as quantification function and some effective indexes for similarity and dissimilarity were computed. The second method was to transform the original spectralvector to encoding vector according to absorption or reflectance feature bands, and similarity measure was conducted to encoding vectors. It proved that the spectral polygon-based approach was effective and can be used to hyperspectral RS image retrieval.

© 2005 Chinese Optics Letters

OCIS Codes
(100.5010) Image processing : Pattern recognition
(280.0280) Remote sensing and sensors : Remote sensing and sensors
(300.6320) Spectroscopy : Spectroscopy, high-resolution

Peijun Du, Tao Fang, Hong Tang, and Pengfei Shi, "Similarity measure of spectral vectors based on set theory and its application in hyperspectral RS image retrieval," Chin. Opt. Lett. 1, 637-640 (2003)

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  1. R. L. Pu and P. Gong, Hyperspectral Remote Sensing and Its Applications (in Chinese) (Higher Education Press, Beijing, 2000).
  2. Y. J. Zhang, Content Based Visual Information Retrieval (in Chinese) (Science in China Press, Beijing, 2003).
  3. J. X. Sun, Modern Pattern Recognition (in Chinese) (National University of Defense Technology Press, Changsha, 2001).
  4. S. Winter, ISPRS J. Photogrammetry & Remote Sensing 55, 189 (2000).

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