Résumé
In this article we present two different approaches for automatic remote sensing image interpretation which are based on a multi-paradigm collaborative framework which uses classification in order to guide the segmentation process. The first approach applies sequentially many one-vs-all class extractors in a manner inspired by cascading techniques in machine learning. The second approach applies many collaborating one-vs-all class extractors in parallel. We show that the collaboration of the segmentation and classification paradigms result in a remarkable reduction of segmentation errors but also in better object classification in comparison to a hybrid pixel-object approach as well as a deep learning approach. (C) 2017 Elsevier Ltd. All rights reserved.