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Unsupervised change detection for multimodal remote sensing images via coupled dictionary learning and sparse coding

Ferraris, Vinicius and Dobigeon, Nicolas and Cruz Cavalcanti, Yanna and Oberlin, Thomas and Chabert, Marie Unsupervised change detection for multimodal remote sensing images via coupled dictionary learning and sparse coding. (2020) In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), 4 May 2020 - 8 May 2020 (Barcelona, Spain).

(Document in English)

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Official URL: https://doi.org/10.1109/ICASSP40776.2020.9053840


Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. The resolution dissimilarity is often bypassed though a simple preprocessing, applied independently on each image to bring them to the same resolution. However, in some important situations, e.g. a natural disaster, the only images available may be those acquired through sensors of different modalities and resolutions. Therefore, it is mandatory to develop general and robust methods able to deal with this unfavorable situation. This paper proposes a coupled dictionary learning strategy to detect changes between two images with different modalities and possibly different spatial and/or spectral resolutions. The pair of observed images is modelled as a sparse linear combination of atoms belonging to a pair of coupled overcomplete dictionaries learnt from the two observed images. Codes are expected to be globally similar for areas not affected by the changes while, in some spatially sparse locations, they are expected to be different. Change detection is then envisioned as an inverse problem, namely estimation of a dual code such that the difference between the estimated codes associated with each image exhibits spatial sparsity. A comparison with state-of-the-art change detection methods evidences the proposed method superiority.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to IEEE editor. The definitive version is available at http://ieeexplore.ieee.org This papers appears in Proceedings of ICCASP 2020. The original PDF of the article can be found at: https://ieeexplore.ieee.org/document/9053840 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
HAL Id:hal-02950724
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Universidade de São Paulo - USP (BRAZIL)
Laboratory name:
FAPESP : Fundação de Amparo à Pesquisa do Estado de São Paulo (Brésil) - ANR : Agence nationale de la recherche (France) - ANITI : Artificial and Natural Intelligence Toulouse Institute (France)
Deposited On:18 Sep 2020 10:23

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