NEW COMBINED PIXEL/OBJECT-BASED TECHNIQUE FOR EFFICIENT URBAN CLASSSIFICATION USING WORLDVIEW-2 DATA

The new advances of having eight bands satellite mission similar to WorldView-2, WV-2, give the chance to address and solve some of the traditional problems related to the low spatial and/or spectral resolution; such as the lack of details for certain features or the inability of the conventional classifiers to detect some land-cover types because of missing efficient spectrum information and analysis techniques. High-resolution imagery is particularly well suited to urban applications. High spectral and spatial resolution of WorldView-2 data introduces challenges in detailed mapping of urban features. Classification of Water, Shadows, Red roofs and concrete buildings spectrally exhibit significant confusion either from the high similarity in the spectral response (e.g. water and Shadows) or the similarity in material type (e.g. red roofs and concrete buildings). This research study assesses the enhancement of the classification accuracy and efficiency for a data set of WorldView-2 satellite imagery using the full 8-bands through integrating the output of classification process using three band ratios with another step involves an object-based technique for extracting shadows, water, vegetation, building, Bare soil and asphalt roads. Second generation curvelet transform will be used in the second step, specifically to detect buildings' boundaries, which will aid the new algorithm of band ratios classification through efficient separation of the buildings. The combined technique is tested, and the preliminary results show a great potential of the new bands in the WV-2 imagery in the separation between confusing classes such as water and shadows, and the testing is extended to the separation between bare soils and asphalt roads. The Integrated band ratio-curvelet transform edge detection techniques increased the percentage of building detection by more than 30%