Robust unmixing of large sets of mixed pixels

In this paper we propose the use of the Randomized Hough Transform algorithm for the determination of the proportions of pure classes present in sets of mixed pixels, for large datasets (for which the deterministic Hough is prohibitively slow) and in the presence of outliers (i.e. in cases that the classical Least Square Error method cannot cope). We show that the Randomized Hough is of constant CPU time, irrespective of the size of the data sets and can be made even more accurate than the deterministic Hough for datasets larger than about 50 sample points. We demonstrate our results both with simulated and laboratory data.