Approaches to classification of multichannel images

Authors:
Vladimir Lukin;Nikolay Ponomarenko;Andrey Kurekin;Kenneth Lever;Oleksiy Pogrebnyak;Luis Pastor Sanchez Fernandez
Affiliations:
Dept of Transmitters, Receivers and Signal Processing, National Aerospace University, Kharkov, Ukraine;Dept of Transmitters, Receivers and Signal Processing, National Aerospace University, Kharkov, Ukraine;Department of Computer Science, School of Engeneering, Cardiff University, Cardiff, UK;Department of Computer Science, School of Engeneering, Cardiff University, Cardiff, UK;Centro de Investigacion en Computacion, Instituto Politecnico Nacional, Mexico D.F., Mexico;Centro de Investigacion en Computacion, Instituto Politecnico Nacional, Mexico D.F., Mexico
Venue:
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2006

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Abstract

The comparison of different approaches to classification of multichannel remote sensing images obtained by spaceborne imaging systems is presented. It is demonstrated that it is reasonable to compress original noisy images with appropriate compression ratio and then to classify the decompressed images rather than original data. Two classifiers are considered: based on radial basis function neural network and support vector machine. The latter one produces slightly better classification results.