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Evaluation of supervised classification algorithms for identifying crops using airborne hyperspectral data

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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 was then compared with conventional methods. The proposed method was found to be most effective for identifying crops using hyperspectral data in an agricultural land area.

Document Type: Research Article


Affiliations: 1: Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1‐1‐1 Yayoi, Bunkyo‐ku, Tokyo 113‐8657, Japan 2: Center for Geo‐Information Technology Integration, PASCO Corporation, Higashiyama Bldg, 1‐1‐2, Higashiyama, Meguro‐ku, Tokyo 153‐0043, Japan

Publication date: May 1, 2006

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