Partially supervised classification of remote sensing images using SVM-based probability density estimation

A general problem of supervised remotely. sensed image classification assumes prior knowledge to be available for all thematic classes that are present in the considered data set. However, the ground truth map representing this prior knowledge usually does not really, describe all the land cover typologies in the image and the generation of a complete training set represents a time-consuming, difficult and expensive task. This problem may play a relevant role in remote sensing data analysis, since it affects the classification performances of supervised classifiers, which erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is proposed, which allows the identification of samples drawn from unknown classes, through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines (SVMs) for the estimation of probability density, functions and on a recursive procedure to generate prior probabilities estimates for both known and unknown classes. For experimental purposes, both a synthetic data set and two real data sets are employed.