Simultaneous Registration and Change Detection in Multitemporal, Very High Resolution Remote Sensing Data

Abstract : In order to exploit the currently continuous streams of massive, multi-temporal, high-resolution remote sensing datasets there is an emerging need to address efficiently the image registration and change detection challenges. To this end, in this paper we propose a modular, scalable, metric free single shot change detection/registration method. The approach exploits a decomposed interconnected graphical model formulation where registration similarity constraints are relaxed in the presence of change detection. The deformation space is discretized, while efficient linear programming and duality principles are used to optimize a joint solution space where local consistency is impo
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Maria Vakalopoulou, Konstantinos Karatzalos, Nikos Komodakis, Nikos Paragios. Simultaneous Registration and Change Detection in Multitemporal, Very High Resolution Remote Sensing Data. 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Jun 2015, Boston, United States. pp.61-69, ⟨10.1109/CVPRW.2015.7301384⟩. ⟨hal-01264072⟩

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