SVM-based Multi-textural Image Classification and Its Uncertainty Analysis

摘要：

Texture analysis,a hot issue in image processing,is a key technique for ground surface object recognition. This paper presents a supervised image classification method based on multiple and multi-scale texture features and support vector machines (SVM). By taking different scales of ground surface features into account,and by feature fusion technique,this method integrates seven-dimensional texture features of different characteristics from GLCM and fractal theory to realize the land use/cover classification. The seven features combine the abilities to describe image textures of different approaches,which can reach better classification performance than any of them and significantly improves the precision of automatic image interpretation. Classification uncertainty is also evaluated and analyzed at the scale of pixel using the extended probability vector and probability entropy model. The imagery used in this research is RADARSAT-1 SAR data.

关键词： Texture analysis Grey Level Co-occurrence Matrix Fractal Support Vector Machines Uncertainty Extended Probability Vector

作者: Yu Zeng Jixian Zhang J.L. Van Genderen Guangliang Wang

作者单位: Chinese Academy of Surveying and Mapping,Beijing 100830,P.R.China University of Twente,PO Box 6,7500 AA Enschede,The Netherlands Chinese Academy of Surveying and Mapping,Beijing 100830,P.R.China China TopRS Technology Co.,Ltd,Bei

会议类型: 国际会议

会议名称: 2011 International Conference on Opto-Electronics Engineering and Information Science(2011光电电子工程与信息科学国际会议 ICOEIS 2011)

会议地点: 西安

会议语种:英文

页码: 363-367

在线出版日期: 2011-12-23（万方平台首次上网日期，不代表论文的发表时间）