One of the problems in semi-supervised land classification tasks lies in improving classification results without increasing
the number of pixels to be labeled. This would be possible if, instead of increasing the amount of data we
increased the reliability of the data. We suggest to replace the random selection by a unsupervised clustering based
selection strategy in building the training data. We use a mode seeking clustering method to search for cluster representatives,
which will be labeled and then used for training. Here an improvement to the result of the clustering algorithm
is introduced by taking advantage of the spatial information in the image. The number of selected samples provided
by the clustering can be reduced by using a spatial-density criterion to dismiss redundant training information. Two
different alternatives are considered for a spatial criterion, one dismisses selected samples in the same neighbourhood
and the other includes the pixel coordinates for giving the spatial information a larger weight in the clustering. Both
alternatives improve the classification-segmentation results. The classification scheme with training selection provides
state-of-the-art pixel classification results using a smaller training set and suggests an alternative to random selection.
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