College of Earth Exploration Science and Technology，Jilin University，Changchun 130012，China

Due to the influence of clouds，rain and other bad weather，optical images are prone to lose data information in ground object classification． Ｒadar images，as active imaging，can well overcome this defect． Part of the Jing-yue Development Zone in Changchun is selected as the research area，using three classification methods，namely minimum distance，maximum likelihood and support vector machine，the Sentinel--1A radar image and Sentinel--2A multi-spectral image are taken as the data source to improve the ground object classification accuracy based on characteristics fusion． The results show that the classification accuracy of characteristics fusion images is significantly higher than that of optical images，and the support vector machine accuracy is the highest compared with the minimum distance and maximum likelihood． In the absence of cloud cover，the classification accuracy of support vector machine after fusion reached 97. 94%，which was 8. 11% higher than that of optical image． In the case of cloud cover，the accuracy of support vector machine after fusion reaches 77. 29%，which is 12. 5% higher than the optical image，especially for the identification accuracy of water area and building area．