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Robust fusion of multi-band images with different spatial and spectral resolutions for change detection

Ferraris, Vinicius and Dobigeon, Nicolas and Wei, Qi and Chabert, Marie Robust fusion of multi-band images with different spatial and spectral resolutions for change detection. (2017) IEEE Transactions on Computational Imaging, 3 (2). 175-186. ISSN 2333-9403

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Official URL: http://dx.doi.org/10.1109/TCI.2017.2692645

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 different kinds of sensors. More precisely, this paper addresses the problem of detecting changes between two multiband optical images characterized by different spatial and spectral resolutions. This sensor dissimilarity introduces additional issues in the context of operational change detection. To alleviate these issues, classical change detection methods are applied after independent preprocessing steps (e.g., resampling) used to get the same spatial and spectral resolutions for the pair of observed images. Nevertheless, these preprocessing steps tend to throw away relevant information. Conversely, in this paper, we propose a method that more effectively uses the available information by modeling the two observed images as spatial and spectral versions of two (unobserved) latent images characterized by the same high spatial and high spectral resolutions. As they cover the same scene, these latent images are expected to be globally similar except for possible changes in sparse spatial locations. Thus, the change detection task is envisioned through a robust multiband image fusion method, which enforces the differences between the estimated latent images to be spatially sparse. This robust fusion problem is formulated as an inverse problem, which is iteratively solved using an efficient block-coordinate descent algorithm. The proposed method is applied to real panchromatic, multispectral, and hyperspectral images with simulated 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:Thanks to IEEE editor. The definitive version is available at http://ieeexplore.ieee.org The original PDF can be found at IEEE Transactions on Computational Imaging (ISSN 2333-9403) website : https://ieeexplore.ieee.org/document/7895167/ 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.
Audience (journal):International peer-reviewed journal
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Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Other partners > Duke University (USA)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (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)
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Deposited By: IRIT IRIT
Deposited On:24 May 2018 10:04

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