Mixed pixel classification with robust statistics

In this paper, we present a novel method for mixed pixel classification where the Hough transform and the trimmed means methods are used to classify small sets of pixels, We compare the performance of these methods with the least squares error method, and we show that in the presence of outliers, the trimmed means method is far more reliable than the traditional least squares error method, and even when no outliers are present, its performance is comparable to that of the least squares error method, The method is exhaustively tested using simulated data, and it is also applied to real Landsat TM data for which ground data are available.