Abstract

An extended CIE transformation was used to deal with the remotely sensed hyperspectral imaging spectrometric data analysis. It was shown in a previous paper [ Appl. Opt. 23, 465 ( 1984)] that this transformation could provide an effective analysis for the contextual identification of minerals, vegetation, and crops. In this study, we shall report a method to monitor the seasonal variations using their respective hyperspectral characteristics for five sets of crops in detail. Based on our mathematical transformation, we found that each class of crops has its unique time-varying spectral characteristics when displayed in the CIE chromaticity diagram. The seasonal growth patterns of wheat, soybean, and corn can be determined and monitored. By using this method one can analyze and reveal the dynamic changes of surface chroma for the renewable resources under study.

© 1984 Optical Society of America

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