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

Li Ni; Bing Zhang; Lianru Gao; Shanshan Li; Yuanfeng Wu

doi:10.1117/12.2015646

23 May 2013 Optimising the use of hyperspectral and multispectral data for regional crop classification

Li Ni, Bing Zhang, Lianru Gao, Shanshan Li, Yuanfeng Wu

Proceedings Volume 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII; 87451V (2013) https://doi.org/10.1117/12.2015646
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

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.

Citation Download Citation

Li Ni, Bing Zhang, Lianru Gao, Shanshan Li, and Yuanfeng Wu "Optimising the use of hyperspectral and multispectral data for regional crop classification", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87451V (23 May 2013); https://doi.org/10.1117/12.2015646

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KEYWORDS

Associative arrays

Hyperspectral simulation

Image classification

Data centers

Data conversion

Spectral resolution

Accuracy assessment

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