An approach for classifying large scale images

In the remote sensing field, classification of images at large scale represents a very important problem. Most of the proposed classification strategies are based on supervised methods, which can give excellent performances, but depend strongly on the training samples used to construct the classification model. In particular, they can fail if such samples are not representative of the distributions associated with the classes. This problem is critical in a large scale scenario, in which the training samples acquired from a limited region of the image, called source domain, are not representative for classifying samples extracted from a different region, called target domain. In this work, we propose to alleviate this problem by adopting an active learning approach, in which few additional samples are selected and labeled from the new domain in order to improve generalization capabilities of the model. In particular, we suggest implementing an initialization strategy before applying the traditional active learning process. The proposed approach is validated experimentally on a MODIS data set for the discrimination between vegetation and non-vegetation areas at European scale.