Multiple kernel relevance vector machine for geospatial objects detection in high-resolution remote sensing images

• Published in: Journal of electronics (China) Vol. 29; no. 5; pp. 353 - 360
• Main Authors: Li, Xiangjuan, Sun, Xian, Wang, Hongqi, Li, Yu, Sun, Hao
• Format: Journal Article
• Language: English
• Published: Heidelberg SP Science Press 01.09.2012; Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Technology in Geo-spatial Information Processing and Application System,Beijing 100190, China
• Subjects: Electrical Engineering; Engineering; Feature extraction; Sliding-window; TP391; Support Vector Machine (SVM); Relevance Vector Machine (RVM); Object detection
• ISSN: 0217-9822; 1993-0615
• DOI: 10.1007/s11767-012-0853-4

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.