SEGMENTATION OF REMOTE-SENSING IMAGES BY SUPERVISED TS-MRF (TP-L4)

Author(s) : Giovanni Poggi (Università di Napoli Federico II, Italy) Giuseppe Scarpa (Università di Napoli Federico II, Italy) Josiane Zerubia (INRIA Sophia Antipolis, France)

Abstract : In this work we specialize the tree-structured MRF model for the supervised segmentation of multispectral satellite images. This model allows a hierarchical representation of a 2-D field by means of a sequence of binary MRFs, each corresponding to a node in the tree. One can fit the intrinsic structure of the data to the TS-MRF model, thereby defining a multi-parameter, flexible, MRF. Although a global MRF model is defined on the whole tree, optimization as well estimation can be carried out by working on a single node at a time, with a significant reduction in complexity. Experiments on a test SPOT image prove the superior performance of the algorithm w.r.t. other MRF-based or variational algorithms for supervised segmentation.

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