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Proceedings Paper

Optimising the use of hyperspectral and multispectral data for regional crop classification
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Paper Abstract

Optical remotely sensed data, especially hyperspectral data have emerged as the most useful data source for regional crop classification. Hyperspectral data contain fine spectra, however, their spatial coverage are narrow. Multispectral data may not realize unique identification of crop endmembers because of coarse spectral resolution, but they do provide broad spatial coverage. This paper proposed a method of multisensor analysis to fully make use of the virtues from both data and to improve multispectral classification with the multispectral signatures convert from hyperspectral signatures in overlap regions. Full-scene crop mapping using multispectral data was implemented by the multispectral signatures and SVM classification. The accuracy assessment showed the proposed classification method is promising.

Paper Details

Date Published: 23 May 2013
PDF: 7 pages
Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87451V (23 May 2013); doi: 10.1117/12.2015646
Show Author Affiliations
Li Ni, Ctr. for Earth Observation and Digital Earth (China)
Univ. of Chinese Academy of Sciences (China)
Bing Zhang, Ctr. for Earth Observation and Digital Earth (China)
Lianru Gao, Ctr. for Earth Observation and Digital Earth (China)
Shanshan Li, Ctr. for Earth Observation and Digital Earth (China)
Yuanfeng Wu, Ctr. for Earth Observation and Digital Earth (China)


Published in SPIE Proceedings Vol. 8745:
Signal Processing, Sensor Fusion, and Target Recognition XXII
Ivan Kadar, Editor(s)

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