Proportion estimation and classification of mixed pixels in multispectral data

Data from the LANDSAT satellite are being extensively utilized in a wide range of applications in agriculture, geology, hydrology, environmental monitoring, and other fields. The multispectral scanner aboard the satellite records the electromagnetic radiation at several wavelengths emanating from the objects within its field of view. Different materials reflect radiation differently at various wavelengths, enabling one to distinguish objects on the basis of their spectral responses recorded at several wavelengths. Whenever the objects being viewed by a multispectral scanner are not large relative to the size of a resolution element, a significant portion of the data recorded will consist of a mixture of the responses of two or more objects. To determine the percent of a region covered by each type of object accurately, it becomes necessary to estimate the proportions of objects within a resolution element. Two types of proportion estimators are examined and compared: an estimator based on maximum likelihood and a shortcut method. Both are tested under point-by-point estimation and estimation with data averaging. The assumption that the class covariance matrices are equal and the assumption that the classes follow normal distributions are also examined. Covariance matrices extracted from LANDSAT data are subjected to a likelihood-ratio testmore » for equality, and the effect of assuming equal covariance matrices when the actual covariances are unequal to various degrees is assessed. A classification procedure based on the L/sub 1/ norm is presented as an alternative to the usual least-squares procedure when normality does not hold. Tests are run to compare the accuracy of the two classification procedures using both normal and nonnormal data. 26 figures, 13 tables.« less