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Proceedings Paper

Algorithm for identification based on spectral diagnostic feature matching technology
Author(s): Zhengchao Chen; Xiurui Geng; Bing Zhang; Qingxi Tong; Lanfen Zheng
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Paper Abstract

Imaging spectrometers acquire images in a large number, narrow, contiguous spectral bands to enable the extraction of reflectance spectra at a pixel scale that can be used for identification. Many identification methods based on the spectra match technique have been developed. Such as spectral angle mapping, binary encoding. But these methods use all the data in the spectral dimension and compare the whole similarity between the reference and test spectrum. Sometimes two different kinds of spectrums may have big similarity, and this results in the wrong identification. There are also many algorithms using waveform characters for identification. However these methods maybe ineffective when the spectra have no diagnostic absorption feature. This paper introduces a new algorithm for identification based on diagnostic feature matching technique. Spectral matching technique and waveform characterization are combined for identification. Instead of matching test spectrum in all the wavelength range, this new algorithm emphasizes diagnostic features' location and only matches several diagnostic features in their most possible locations. To insure the idenfication of accuracy, spectral characters in terms of slope and asymmetry are used to check and verify. The algorithm is processed in four steps which will be described in the second part of this paper. In the third part, this algorithm is tested by identifying Alunite from AVIRIS image in Cuprite, Colorado. The result proved this new algorithm effective.

Paper Details

Date Published: 25 September 2003
PDF: 4 pages
Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); doi: 10.1117/12.538637
Show Author Affiliations
Zhengchao Chen, Institute of Remote Sensing Applications, CAS (China)
Xiurui Geng, Institute of Remote Sensing Applications, CAS (China)
Bing Zhang, Institute of Remote Sensing Applications, CAS (China)
Qingxi Tong, Institute of Remote Sensing Applications, CAS (China)
Lanfen Zheng, Institute of Remote Sensing Applications, CAS (China)


Published in SPIE Proceedings Vol. 5286:
Third International Symposium on Multispectral Image Processing and Pattern Recognition
Hanqing Lu; Tianxu Zhang, Editor(s)

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