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Evaluation of supervised classification algorithms for identifying crops using airborne hyperspectral data
International Journal of Remote Sensing
Sufficient training data must be acquired to classify areas of interest using a supervised classification method and hyperspectral data. However, the relatively small size of agricultural plots in Japan means that there is no training area large enough to represent a feature of interest. In this study, a new method for identifying crops using hyperspectral remotely sensed data has been proposed in order to resolve the problem of identifying training areas in agricultural crops. This method wasdoi:10.1080/01431160500380455 fatcat:4g3g4lffhbbtri2t6oo2sh7iwu