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The aim of this paper is to address the monitoring of the recovery phase in the aftermath of Hurricane Matthew (28 September–10 October 2016) in the town of Jérémie, southwestern Haiti. This is accomplished via a novel change detection method that has been formulated, in a data fusion perspective, in terms of multitemporal supervised classification. The availability of very high resolution images provided by last-generation satellite synthetic aperture radar (SAR) and optical sensors makes this analysis promising from an application perspective and simultaneously challenging from a processing viewpoint. Indeed, pursuing such a goal requires the development of novel methodologies able to exploit the large amount of detailed information provided by this type of data. To take advantage of the temporal and spatial information associated with such images, the proposed method integrates multisensor, multisource, and contextual information. Markov random field modeling is adopted here to integrate the spatial context and the temporal correlation associated with images acquired at different dates. Moreover, the adoption of a region-based approach allows for the characterization of the geometrical structures in the images through multiple segmentation maps at different scales and times. The performances of the proposed approach are evaluated on multisensor pairs of COSMO-SkyMed SAR and Pléiades optical images acquired over Jérémie, in the aftermath of and during the three years after Hurricane Matthew. The effectiveness of the change detection results is analyzed both quantitatively, through the computation of accuracy measures on a test set, and qualitatively, by visual inspection of the classification maps. The robustness of the proposed method with respect to different algorithmic choices is also assessed, and the detected changes are discussed in relation to the recovery endeavors in the area and ground-truth data collected in the field in April 2019. [ABSTRACT FROM AUTHOR]

The proposed classification approach integrates multi-sensor, multi-source and contextual information associated with COSMO-SkyMed (CSK) and Pléiades images, thus formalizing the change-detection task in terms of a data fusion problem. Multi-temporal fusion "at the decision level" is used in this work. In particular, the change detection problem is operatively addressed through a multi-temporal supervised classification of multi-sensor CSK/Pléiades data. The method makes use of the Markov Random Field (MRF) theory to integrate the spatial context and the temporal correlation associated with the images acquired at the different dates, and is fully integrated with a region-based approach able to characterize the geometrical structures in the images through the employment of multiple segmentation maps at different scales. The approach has been applied to bespoke multi-temporal series consisting of pairs of CSK/Pléiades images that have been acquired as much as possible simultaneously, and provided by the Italian Space Agency (ASI) and the Centre National d'Études Spatiales (CNES) in the framework of CEOS (Committee on Earth Observation Satellites) Working Group on Disasters activities. The target is to monitor the changes in land cover classes. In order to exploit the potential of the change detection methodology and the spatial information of very high resolution (VHR) imagery, the proposed approach is implemented in the two following steps: - a first analysis conducted on the acquired imagery subsampled at a resolution of 10 m;- a second analysis performed on particularly interesting areas highlighted during the first step, making use of the imagery at the maximum available resolution.The target of the project is to test the potential of the proposed approach to monitor changes in the land use/land cover that can be related to:(i) the recovery phase after a natural disaster;(ii) the implementation of Disaster Risk Reduction (DRR) policies (e.g. monitoring of Sendai Framework Assessment - SFA indicators).Two case studies are presented:(i) The monitoring of the recovery phase in the aftermath of Hurricane Matthew in Haiti, which struck the Caribbean in October 2016, with a dataset composed of CSK/Pléiades pairs acquired in November 2015 (before the event), in October 2016 (few days after the event) and a year later, in October 2017.(ii) The monitoring of the surroundings of the city of Phnom Penh, Cambodia, at different time steps. The target is to monitor the changes in land cover classes related to SFA indicators. The focus is on a subset of the indicators C and D, which can be directly related to specific land cover classes. In particular C2-C, C-2FO, C-3, C-4, C-5, D2, D3, and D4.The work presented in this abstract is framed within the activity of the CEOS-DRM project (Committee on Earth Observation Satellites – Disaster Risk Management) funded by ASI. [ABSTRACT FROM AUTHOR]