Multi-Objects Detection in Remote Sensing Images Using Multiple Kernel Learning

摘要：

　　The objective of this work is multiple objects detection in remote sensing images.Many classifiers have been proposed to detect military objects.In this paper,we demonstrate that linear combination of kernels can get a better classification precision than product of kernels.Starting with base kernels,we obtain different weights for each class through learning.Experiment on Caltech-lO1 dataset shows the learnt kernels yields superior classification results compared with single-kernel SVM.While such a powerful classifier act as a sliding-window detector to search planes in images collected from Google Earth,results shows the effectiveness of using MKL detector to locate military objects in remote sensing images.

关键词： Object detection Multiple kernel learning SVM Feature extraction Sliding-window

作者: Xiangjuan Li Hao Sun Xinwei Zheng Xian Sun Hongqi Wang

作者单位: Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;Key Laboratory of Techn Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;Key Laboratory of Techn

会议类型: 国际会议

会议名称: 2012 2nd international Conference on Materials Science and Information Technology(2012第二届材料科学与信息技术国际会议)(MSIT2012)

会议地点: 西安

会议语种:英文

页码: 1258-1262

在线出版日期: 2012-08-24（万方平台首次上网日期，不代表论文的发表时间）