Nonparametric classification of SAR data based on a modified iterated nearest-mean reclustering of pixel features

This work describes a nonparametric algorithm suitable for scene classification, either supervised or not, starting from a number of pixel features derived from SAR observations. Pixel vectors composed by simple features derived from the backscatter coefficients of one or more bands and/or polarizations are iteratively clustered into dynamically upgraded classes. Possible "a priori" knowledge coming from ground truth data may be used to initialize the procedure, but is not mandatory. Experiments on MAC-91 NASA/JPL AIRSAR data on the Montespertoli test site show that seven features derived from each of L-HV and P-HV observations are capable to discriminate seven agricultural cover classes with an overall pixel accuracy of 60%, when the algorithm learns from 10% of the truth data and classifies the remaining 90%.