Improving Hyperspectral Pixel Classification With Unsupervised Training Data Selection

Abstract

An unsupervised method for selecting training data is suggested here. The method is tested by applying it to hyperspectral land-use classification. The data set is reduced using an unsupervised band selection method and then clustered with a nonparametric cluster technique. The cluster technique provides centers of the clusters, and those are the samples selected to compose the training set. Both the band selection and the clustering are unsupervised techniques. Afterward, an expert labels those samples, and the rest of unlabeled data can be classified. The inclusion of the selection step, although unsupervised, allows to select automatically the most suitable pixels to build the classifier. This reduces the expert effort because less pixels need to be labeled. However, the classification results are significantly improved in comparison with the results obtained by a random selection of training samples, in particular for very small training sets