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Motivated by the increasing importance of hyperspectral remote sensing, this study investigates the potential of the current-generation satellite hyperspectral data for coastal water mapping. Two narrow-band Hyperion images, acquired in summer 2004 within a nine day period, were used. The study area is situated at the northern sector of south Evvoikos Gulf, in Central Greece. Underwater springs, inwater streams, urban waste and industrial waste are present in the gulf. Thus, further research regarding the most appropriate methods for coastal water mapping is advisable. In situ measurements with a GPS have located the positions of all sources of water and waste. At these positions groundspectro-radiometer measurements were also implemented. Two different approaches were used for the reduction of the Hyperion bands. First, on the basis of histogram statistics the uncalibrated bands were selected and removed. Then the Minimum Noise Fraction was used to classify the bands according to their signal to noise ratio. The noisiest bands were removed and thirty-eight bands were selected for further processing. Second, mathematical and statistical criteria were applied to the in situ radiometer measurements of reflectance and radiance in order to identify the most appropriate parts of the spectrum for the detection of underwater springs and urban waste. This approach has determined nine hyperspectral bands. Τhe Pixel Purity Index and the n-D Visualiser methods were used for the identification of the spectra endmembers. Both whole (Spectral Angle Mapper or Spectral Feature Fitting) and sub pixel methods (Linear Unmixing or Mixture-Tuned Matched Filtering) were used for further analysis and classification of the data. Bands resulting from processing the groundspectro-radiometer measurements produced the highest classification results. The spatial resolution of the Hyperion hyperspectral data hardly allows the detection and classification of underwater springs. Contrary, inwater streams and chlorophyll are satisfactorily classified. The SAM classification method seems to work better as the number of endmembers increases. The Linear Unmixing classification method gives better results as the number of endmembers decreases.
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