Abstract: A new object-oriented method for classification of high resolution remotely sensed imagery is proposed in the paper, which integrates support vector machine (SVM) technique with rough-set-based granular computing (RSBGC). First, gradient images are obtained by applying phase congruency model to the IKONOS panchromatic image. Extended minima transform and minima imposition are used to get foreground marking of interesting objects and implement gradient reconstruction respectively. Based on these improvement measures, better segmentation is achieved using watershed transform. Second, spectral characteristic is got from multi-spectral data and texture feature is extracted by Gabor wavelet. Multi-kernel SVM is used to present preparatory object-oriented classification, and information granularities are obtained through intersection of the classification results. Third, granularities are differentiated by means of comparing the Euclidean distance between average value of granularity and every sample central moment. Spatial adjacency relation among the granularities is quantitative analyzed in order to classify the uncertain granularities after the former clustering. The resulting classification is achieved by little artificial interaction identification. A comparative experiment is performed with both SVM and neural network methods based on RBF-kernel function. It is shown that the proposed method can obtain better classification results.