Traditional classification methods only based on spectrum features of pixels are not suitable for high-resolution remote sensing image. A new object-oriented classification method is proposed based on quotient space granular theory, cloud model, fuzzy support vector machines and decision tree algorithm. Considering that the two factors which determinate the accuracy of classification results are image segmentation and classification algorithm, we make some improvements and our work includes the following aspects. Firstly, an adaptive region growing method is proposed based on the Cloud Model. Secondly, a hierarchy-synthesis classification technique is proposed based on quotient space granular theory, fuzzy support vector machine and decision tree. A new algorithm is designed to calculate the fuzzy membership of sample. By combining FSVM with ISODATA, we not only improve the quality of training sample, but also make objects classified automatically. More importantly, the strategy of hierarchical classification makes different categories discriminated more effectively. By using synthesis theory of quotient space, we can get the final classification result from classification results of multi-granular quotient spaces. According to the experiment result, our method can not only improve the accuracy of classification result and satisfy human eye, but also make objects classified automatically.