Optimizing statistical classification accuracy of satellite remotely sensed imagery for supporting fast flood hydrological analysis
Journal
Acta Geophysica
Date Issued
April 10, 2012
DOI
10.2478/s11600-012-0025-9
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 aboratory 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 differentclassification algorithms (ISODATA, maximum likelihood, object based,
maximum entropy) to satellite images acquired during time period when
precipitation phenomena had been recorded.
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 differentclassification algorithms (ISODATA, maximum likelihood, object based,
maximum entropy) to satellite images acquired during time period when
precipitation phenomena had been recorded.