Classification of High Resolution Satellite Images Using Texture from the Panchromatic Band
Traditional classification algorithms are not suitable for feature extraction on high resolution satellite images, given the heterogeneity of the pixels of this type of imagery due to a great amount of detail. Most of this type of imagery is taken by the satellite in several bands and at different resolutions, and the method presented in this paper takes advantage of this situation, merging information provided by the multispectral bands with the panchromatic band. An Ikonos image of 2 x 2 km of the university campus of Alcala has been used for obtaining a classification with seven land use classes. A comparison is carried out between the traditional maximum likelihood method and the method developed here. The latter using context information obtained by the texture from the band with the maximum resolution, the panchromatic band. The results show how texture information improves maximum likelihood classification of the multispectral bands for smooth-textured classes.
KeywordsMaximum Likelihood Algorithm Maximum Likelihood Classifier Maximum Likelihood Classification Ikonos Image Panchromatic Band
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