Abstract
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.
Highlights
IntroductionAdvances in the design and development of optical and synthetic aperture radar (SAR) satellite sensors have favored the deployment of new technological solutions able to acquire imagery at very high spatial resolution (VHR) with short revisit time
This article is an open access articleIn the last decades, advances in the design and development of optical and synthetic aperture radar (SAR) satellite sensors have favored the deployment of new technological solutions able to acquire imagery at very high spatial resolution (VHR) with short revisit time
The proposed approach has been applied to the case study described in Section 3.1 and associated with optical (Pléiades) and SAR (COSMO-SkyMed) data collected between
Summary
Advances in the design and development of optical and synthetic aperture radar (SAR) satellite sensors have favored the deployment of new technological solutions able to acquire imagery at very high spatial resolution (VHR) with short revisit time. In this context, the possibility of using pattern recognition and image processing techniques for the automatic processing of such a variety of remote sensing data represents an effective approach to tackle applications such as environmental monitoring, environmental disaster management, and disaster prevention tasks. Being able to take advantage of heterogeneous data (for example, by jointly processing pairs of images taken at different times and possibly by different sensors and at different resolutions, independently of the acquisition characteristics) to highlight the temporal evolution of the. The main opportunity is to take advantage of the complementarity of such information sources to map effectively the different types of change that may occur between two different moments in time, in either a short-term or a long-term scenario
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