Collaborative segmentation and classification for remote sensing image analysis

Andres Troya-Galvis, Pierre Gançarski, Laure Berti-Équille

Abstract: In this article we present CoSC, a generic framework for collaborative segmentation and classification. The framework is guided by both radiometric homogeneity based criteria and implicit semantic criteria to segment and extract the objects of a given thematic class. We present a proof-of-concept case-study and show that CoSC is able to reach higher confidence for object classification and results in significant improvement of the whole segmentation.