5 February 2004 SVM-based density estimation for supervised classification of remotely sensed images with unknown classes
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Abstract
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 training set representing this prior knowledge usually does not really describe all the land cover typologies in the image and the generation of a complete training data set would be 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, that 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 of unknown classes, through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines for the estimation of probability density functions and on a recursive procedure to generate prior probabilities estimates for both known and unkown classes. For experimental purposes, both a synthetic and a real data set are considered.
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Paolo Mantero, Paolo Mantero, Gabriele Moser, Gabriele Moser, Sebastiano B Serpico, Sebastiano B Serpico, } "SVM-based density estimation for supervised classification of remotely sensed images with unknown classes", Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); doi: 10.1117/12.514179; https://doi.org/10.1117/12.514179
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