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Coupled dictionary learning for unsupervised change detection between multimodal remote sensing images

Ferraris, Vinicius and Dobigeon, Nicolas and Cruz Cavalcanti, Yanna and Oberlin, Thomas and Chabert, Marie Coupled dictionary learning for unsupervised change detection between multimodal remote sensing images. (2019) Computer Vision and Image Understanding, 189. 1-15. ISSN 1077-3142

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Official URL: https://doi.org/10.1016/j.cviu.2019.102817

Abstract

Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations, the only images available may be those acquired through sensors of different modalities. This paper addresses the problem of unsupervisedly detecting changes between two observed images acquired by sensors of different modalities with possibly different resolutions. These sensor dissimilarities introduce additional issues in the context of operational change detection that are not addressed by most of the classical methods. This paper introduces a novel framework to effectively exploit the available information by modeling the two observed images as a sparse linear combination of atoms belonging to a pair of coupled overcomplete dictionaries learnt from each observed image. As they cover the same geographical location, codes are expected to be globally similar, except for possible changes in sparse spatial locations. Thus, the change detection task is envisioned through a dual code estimation which enforces spatial sparsity in the difference between the estimated codes associated with each image. This problem is formulated as an inverse problem which is iteratively solved using an efficient proximal alternating minimization algorithm accounting for nonsmooth and nonconvex functions. The proposed method is applied to real images with simulated yet realistic and real changes. A comparison with state-of-the-art change detection methods evidences the accuracy of the proposed strategy.

Item Type:Article
Additional Information:https://www.sciencedirect.com/science/article/pii/S1077314219301274
HAL Id:hal-02397250
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
Funders:
CAPES : Coordenação de Aperfeiçoa-mento de Ensino Superior (Brazil) - EU FP7 through the ERANETMED JC-WATER program (Europe) - ANR : Agence Nationale de la Recherche (France)
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Deposited By: IRIT IRIT
Deposited On:29 Nov 2019 14:50

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