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Optimizing statistical classification accuracy of satellite remotely sensed imagery for supporting fast flood hydrological analysis

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Abstract

The aim of this study is to improve classification results of multispectral satellite imagery for supporting flood risk assessment analysis in a catchment area in Cyprus. For this purpose, precipitation and ground spectroradiometric data have been collected and analyzed with innovative statistical analysis methods. Samples of regolith and construction material were in situ collected and examined in the spectroscopy laboratory for their spectral response under consecutive different conditions of humidity. Moreover, reflectance values were extracted from the same targets using Landsat TM/ETM+ images, for drought and humid time periods, using archived meteorological data. The comparison of the results showed that spectral responses for all the specimens were less correlated in cases of substantial humidity, both in laboratory and satellite images. These results were validated with the application of different classification algorithms (ISODATA, maximum likelihood, object based, maximum entropy) to satellite images acquired during time period when precipitation phenomena had been recorded.

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Authors and Affiliations

  1. Department of Civil Engineering and Geomatics, Remote Sensing Lab., Faculty of Engineering and Technology, Cyprus University of Technology, Limassol, Cyprus

    Dimitrios D. Alexakis, Athos Agapiou & Diofantos G. Hadjimitsis

  2. National Observatory of Athens, Institute for Environmental Research and Sustainable Development, Athens, Greece

    Adrianos Retalis

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  1. Dimitrios D. Alexakis
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  2. Athos Agapiou
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  3. Diofantos G. Hadjimitsis
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  4. Adrianos Retalis
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Correspondence to Dimitrios D. Alexakis.

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Alexakis, D.D., Agapiou, A., Hadjimitsis, D.G. et al. Optimizing statistical classification accuracy of satellite remotely sensed imagery for supporting fast flood hydrological analysis. Acta Geophys. 60, 959–984 (2012). https://doi.org/10.2478/s11600-012-0025-9

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  • DOI: https://doi.org/10.2478/s11600-012-0025-9

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