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Recent developments in satellite sensors tend to the availability of highspatial and spectral resolution images. The motivation of this researchpaper is to get maximum benefits of different bands in high resolutionsatellite images. In this research paper, a novel classification technique isintroduced where the shared texture features properties problem isaddressed. Atmospheric correction is applied on a high resolution WorldView 2 (WV2) image to produce reflectance value for all spectral bands.Reflectance image is produced by knowing the environmental parametersof the images at the capturing time, which can be extracted from auxiliaryfiles associated with the input image. A multi-layer classification treeanalysis is applied on a reflectance image to extract urban area featuresbased on investigated thresholding values. The proposed technique isinvestigated through MATLAB environment. The results of the proposedtechnique are assessed versus classification results of MaximumLikelihood classification technique that is applied through ENVI software.The assessment of classification results is represented in confusion matrixformat and determination of Kappa Coefficient. The investigatedtechnique succeeded in classifying urban area features up to 90%. Theproposed technique is fast, automated and suitable for any image withsame spectral bands as WV2 satellite image.

A multi-layer Classification Technique for High Resolution Satellite Images Using Radiometric Calibration Modelling