MULTIPLE KERNEL RELEVANCE VECTOR MACHINE FOR GEOSPATIAL OBJECTS DETECTION IN HIGH-RESOLUTION REMOTE SENSING IMAGES  

作　　者：Li Xiangjuan Sun Xian Wang Hongqi Li Yu Sun Hao 

机构地区：[1]Institute of Electronics, Chinese Academy of Sciences [2]Key Laboratory of Technology in Geo-spatial Information Processing and Application System [3]Graduate University, Chinese Academy of Sciences

出　　处：《Journal of Electronics(China)》2012年第5期353-360,共8页电子科学学刊（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No.41001285)

摘　　要：Geospatial objects detection within complex environment is a challenging problem in remote sensing area. In this paper, we derive an extension of the Relevance Vector Machine (RVM) technique to multiple kernel version. The proposed method learns an optimal kernel combination and the associated classifier simultaneously. Two feature types are extracted from images, forming basis kernels. Then these basis kernels are weighted combined and resulted the composite kernel exploits interesting points and appearance information of objects simultaneously. Weights and the detection model are finally learnt by a new algorithm. Experimental results show that the proposed method improve detection accuracy to above 88%, yields good interpretation for the selected subset of features and appears sparser than traditional single-kernel RVMs.Geospatial objects detection within complex environment is a challenging problem in re- mote sensing area. In this paper, we derive an extension of the Relevance Vector Machine （RVM） technique to multiple kernel version. The proposed method learns an optimal kernel combination and the associated classifier simultaneously. Two feature types are extracted from images, forming basis kernels. Then these basis kernels are weighted combined and resulted the composite kernel exploits interesting points and appearance information of objects simultaneously. Weights and the detection model are finally learnt by a new algorithm. Experimental results show that the proposed method improve detection accuracy to above 88%, yields good interpretation for the selected subset of features and appears sparser than traditional single-kernel RVMs.

关 键 词：Object detection Feature extraction Relevance Vector Machine (RVM) Support Vector Machine (SVM) Sliding-window 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]