polsar classification using contextual based locality preserving projection and guided filtering

contextual feature extraction is studied for polarimetric synthetic aperture radar (polsar) image classification in this work. the contextual locality preserving projection (clpp) method is proposed for generation of contextual feature cubes using limited training samples. the local information in neighborhood regions is used to extend the training set by including the spatial information. then, a supervised transform is applied to the polarimetric-contextual feature cube to reduce the data dimensionality while preserves the local structures and settles the samples belonging to the same class close together. finally, a guided filter is applied to the classification map to degrade the speckle noise. the classification results on two real l-band polsar data from airsar show superior performance of clpp for polsar classification in small sample size situations.