Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7837
Title: Optimizing statistical classification accuracy of satellite remotely sensed imagery for supporting fast flood hydrological analysis
Authors: Alexakis, Dimitrios 
Hadjimitsis, Diofantos G. 
Retalis, Adrianos 
Agapiou, Athos 
Keywords: Spectroradiometer;Statistics;Classification;Remote sensing
Category: Civil Engineering
Field: Engineering & Technology
Issue Date: 2012
Link: http://link.springer.com/article/10.2478/s11600-012-0025-9
Series/Report no.: Acta Geophysica, 2012, Volume 60, Issue 3, Pages 959-984
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.
URI: http://ktisis.cut.ac.cy/handle/10488/7837
ISSN: 1895-7455
DOI: 10.2478/s11600-012-0025-9
Rights: © 2012 Institute of Geophysics, Polish Academy of Sciences
Type: Article
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