Airborne imaging spectroscopy data (AISA Eagle and HyMap) were applied to classify the sediments of a sandy beach in seven sand type classes. On the AISA-Eagle data, several classification strategies were tried out and compared with each other. The best classification results were obtained applying a linear discriminant classifier (LDC) in combination with feature selection based on sequential floating forward search (SFFS). The statistical LDC was used in a multiple binary approach. In the first step, the original bands were used in the classification, but transformation of the bands to wavelet coefficients enhanced the accuracy obtained. The combination of LDC with SFFS resulted in an overall accuracy of 82% (using three wavelet coefficients). Replacing the LDC with the non-statistical SAM algorithm reduced the overall accuracy to 74% (using all bands or wavelet coefficients). When applying LDC, the optimal number of bands/wavelet coefficients to be used was defined: using more than two bands or three wavelet coefficients did not result in a higher classification accuracy. Finally, the HyMap data, featuring 126 bands in the VNIR-SWIR range, were used to demonstrate that the VNIR range outperforms the SWIR range for this application.
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Document Type: Research Article
Remote Sensing and Earth Observation Processes (TAP), Flemish Institute for Technological Research (VITO), Boeretang 200, 2400-Mol, Belgium
Renard Centre of Marine Geology, Ghent University, Krijgslaan 281-S8, 9000-Gent, Belgium
January 1, 2008
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