摘要
从高分遥感影像中提取车辆信息,对民用和军事领域具有重要意义。为提高车辆信息提取的精度和效率,提出SURF特征和支持向量机(SVM)相结合的方法,对感兴趣区域的车辆进行提取。通过边缘信息消除冗余图像,利用半搜索策略滑动窗口,以提高车辆识别精度,减少计算量。对深圳南山区0.25 m分辨率的遥感影像进行车辆提取测试,测试结果表明:车辆提取的错误率低于20%;车辆提取时间控制在分钟级,本算法具有一定的工程适用性。
Vehicle information extracted from high resolution remote sensing image is of great significance in civil and military fields. To improve the accuracy and efficiency of the vehicle information extraction, the combined SURF（speeded up robust features） and support vector machine（SVM） algorithm is proposed to extract the vehicle information of the interest region. The edge information redundancy eliminating method and semi-search strategy are used to enhance the identification accuracy and reduce the amount of calculation. Vehicle information in the 0.25 m resolution remote sensing image of Nanshan District in Shenzhen is extracted and tested, the results show that the false rate is less than 20% and the extraction time can be controlled in minute level. The applicability of the method is demonstrated.
出处
《湖南工业大学学报》
2014年第2期67-71,共5页
Journal of Hunan University of Technology
基金
中国交通运输部重点基金资助项目(2012-364-208-802-2)
