Improved Partition Trees for Multi-Class Segmentation of Remote Sensing Images

Emmanuel Maggiori 1, 2, * Yuliya Tarabalka 1, 2 Guillaume Charpiat 3, 4
* Corresponding author
2 TITANE - Geometric Modeling of 3D Environments
CRISAM - Inria Sophia Antipolis - Méditerranée
3 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
4 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : We propose a new binary partition tree (BPT)-based framework for multi-class segmentation of remote sensing images. In the literature, BPTs are typically computed in a bottom-up manner based on spectral similarities, then analyzed to extract image objects. When image objects exhibit a considerable internal spectral variability, it often happens that such objects are composed of several disjoint regions in the BPT, yielding errors in object extraction. We pose the multi-class segmentation problem as an energy minimization task and solve it by using BPTs. Our main contribution consists in introducing a new dissimilarity function for the tree construction , which combines both spectral discrepancies and supervised class-specific information to take into account the within-class spectral variability. The experimental validation proved that the proposed method constitutes a competitive alternative for object-based image classification.
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Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat. Improved Partition Trees for Multi-Class Segmentation of Remote Sensing Images. 2015 IEEE International Geoscience and Remote Sensing Symposium - IGARSS 2015, IEEE, Jul 2015, Milan, Italy. ⟨hal-01182772⟩

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