Advanced active sampling for remote sensing image classification

A novel approach to active sampling is proposed for the semi automatic selection of training patterns in a given pool of candidates. In the method proposed, each candidate is ranked with a double criterion: first, informativeness of the candidate is assessed using the Support Vector Machine (SVM) real valued decision function. Then, diverse sampling is ensured by considering the relative position of the candidate in the SVM feature space. Such position is evaluated by using partitioning of the feature space via nonlinear clustering. Among all candidates belonging to the same cluster, the winner is the pixel minimizing the weighted combination of the distances from both the SVM hyperplane and the cluster center. This way, the proposed approach provides a way to i) account for and minimize the redundancy of the sampled pixels and ii) maximize the speed of convergence to an optimal classification accuracy. In order to discover clusters and to evaluate the distance between the cluster center and the samples, the kernel k-means algorithm is used in a hierarchical way. By its kernel nature, the algorithm partitions data in the SVM induced space, thus ensuring coherent diversity with respect to the linear model therein. The reliability of the proposed heuristic is evaluated on a QuickBird VHR image of the city of Zurich, Switzerland.