Remote sensing image analysis by aggregation of segmentation-classification collaborative agents. (January 2018) - Troya-Galvis, Andrés; Gançarski, Pierre; Berti-Équille, Laure
Highlights: Two new approaches for collaborative remote sensing image analysis are presented. They both are based on a multi-paradigm framework which uses classification to guide a segmentation process. The proposed methods aggregate many mono-class extractors in order to make multi-class remote sensing image classification. Experiments show that the proposed methods give better results (both in terms of classification and segmentation) than a hybrid object-based approach as well as a deep learning approach, even if the training data is limited in quantity and quality. Abstract: 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.