18 October 2016 Flood mapping using VHR satellite imagery: a comparison between different classification approaches
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Abstract
Various regions in Europe have suffered from severe flooding over the last decades. Flood disasters often have a broad extent and a high frequency. They are considered the most devastating natural hazards because of the tremendous fatalities, injuries, property damages, economic and social disruption that they cause. In this context, Earth Observation techniques have become a key tool for flood risk and damage assessment. In particular, remote sensing facilitates flood surveying, providing valuable information, e.g. flood occurrence, intensity and progress of flood inundation, spurs and embankments affected/threatened. The present work aims to investigate the use of Very High Resolution satellite imagery for mapping flood-affected areas. The case study is the November 2013 flood event which occurred in Sardinia region (Italy), affecting a total of 2,700 people and killing 18 persons. The investigated zone extends for 28 km2 along the Posada river, from the Maccheronis dam to the mouth in the Tyrrhenian sea. A post-event SPOT6 image was processed by means of different classification methods, in order to produce the flood map of the analysed area. The unsupervised classification algorithm ISODATA was tested. A pixel-based supervised technique was applied using the Maximum Likelihood algorithm; moreover, the SPOT 6 image was processed by means of object-oriented approaches. The produced flood maps were compared among each other and with an independent data source, in order to evaluate the performance of each method, also in terms of time demand.
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Francesca Franci, Francesca Franci, Piero Boccardo, Piero Boccardo, Emanuele Mandanici, Emanuele Mandanici, Elena Roveri, Elena Roveri, Gabriele Bitelli, Gabriele Bitelli, } "Flood mapping using VHR satellite imagery: a comparison between different classification approaches", Proc. SPIE 10005, Earth Resources and Environmental Remote Sensing/GIS Applications VII, 1000509 (18 October 2016); doi: 10.1117/12.2241390; https://doi.org/10.1117/12.2241390
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