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

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.