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Erschienen in:
2012 | OriginalPaper | Buchkapitel
SVM and Haralick Features for Classification of High Resolution Satellite Images from Urban Areas
verfasst von : Aissam Bekkari, Soufiane Idbraim, Azeddine Elhassouny, Driss Mammass, Mostafa El yassa, Danielle Ducrot
Erschienen in: Image and Signal Processing
Verlag: Springer Berlin Heidelberg
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The classification of remotely sensed images knows a large progress taking in consideration the availability of images with different resolutions as well as the abundance of classification’s algorithms. A number of works have shown promising results by the fusion of spatial and spectral information using Support vector machines (SVM). For this purpose we propose a methodology allowing to combine these two informations using a combination of multi-spectral features and Haralick texture features as data source with composite kernel. The proposed approach was tested on common scenes of urban imagery. The results allow a significant improvement of the classification performances when compared with the two sets of attributes used separately. The experimental results indicate an accuracy value of 93.29% which is very promising.