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浙江大学学报(工学版)
浙江大学学报(工学版)  2019, Vol. 53 Issue (9): 1741-1748    DOI: 10.3785/j.issn.1008-973X.2019.09.013
计算机科学与人工智能     
基于长方形点过程的遥感图像汽车提取
余煇(),柴登峰*()
浙江大学 空间信息技术研究所，浙江 杭州 310058
Vehicle extraction from remotely sensed images based on rectangle marked point processes
Hui YU(),Deng-feng CHAI*()
Institute of Space Information and Technique, Zhejiang University, Hangzhou 310058, China
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摘要：

针对遥感图像中汽车的识别和提取问题，分析大规模数量汽车的分布和排列，提出基于长方形点过程的汽车分布概率模型. 采用先验模型刻画汽车在空间中的分布规律，采用数据项表达模型与图像的联系. 采用模拟退火策略结合可逆跳马尔科夫链蒙特卡洛采样方法，对模型进行优化求解，实现图像中汽车的自动识别和提取. 实验结果表明，所提方法能很好地用于道路和停车场等规则场景，提取精确度达到99%，回收率达到90%；由于先验模型的约束可以很好地解决汽车相互重叠和方向摇摆的问题，汽车提取的效果比传统算法更好.
关键词： 长方形点过程;  汽车提取;  模板匹配;  模拟退火;  可逆跳马尔科夫链蒙特卡洛采样    
Abstract:

A rectangle marked point processes was proposed for vehicle identification and extraction from remotely sensed images, and to analyze the distribution and arrangement of a large number of vehicles. A prior model was developed to represent spatial distribution of vehicles, and a data term was applied to establish the relationship between the model and image.The optimal configuration was searched by simulated annealing coupled with Reversible Jump Markov Chain Monte Carlo, so that vehicles could be identified and extracted automatically. The experimental results show that the proposed method works well for the scenes of road and parking lot. The extraction precision is 99%, and the recall is about 90%. The constrain of prior model can solve the problem of vehicle overlapping and uncertain direction well, and the extraction results are better than that of the traditional algorithm.
Key words: rectangle marked point processes    vehicle extraction    template match    simulated annealing    Reversible Jump Markov Chain Monte Carlo
收稿日期: 2018-07-21 出版日期: 2019-09-12
CLC:  TP 751.1  
通讯作者: 柴登峰     E-mail: 21438031@zju.edu.cn;chaidf@zju.edu.cn
作者简介: 余煇（1992—），男，硕士生，从事遥感图像提取算法研究. orcid.org/0000-0002-6600-4739. E-mail： 21438031@zju.edu.cn
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引用本文:

余煇,柴登峰. 基于长方形点过程的遥感图像汽车提取[J]. 浙江大学学报(工学版), 2019, 53(9): 1741-1748.

Hui YU,Deng-feng CHAI. Vehicle extraction from remotely sensed images based on rectangle marked point processes. Journal of ZheJiang University (Engineering Science), 2019, 53(9): 1741-1748.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.09.013    或    http://www.zjujournals.com/eng/CN/Y2019/V53/I9/1741

图 1  图像中汽车的几何模型
图 2  汽车分布的重叠关系
图 3  汽车分布的方向关系
实验序号 λF α β
1 100 1.0 1.0
2 100 0 1.0
3 1 000 0 1.0
4 1 000 0 0.9
表 1  先验模型中的不同参数设置
图 4  不同参数下的汽车分布结果
图 5  模板匹配处理的图像结果
图 6  道路上汽车图像的提取结果
图 7  广场上汽车图像的提取结果
图像编号 NTP NFP NFN PN RN
1 112 1 8 0.991 2 0.933 3
2 99 1 3 0.990 0 0.970 6
3 802 4 71 0.995 0 0.918 7
4 323 1 18 0.996 9 0.947 2
5 178 1 2 0.994 4 0.988 9
表 2  汽车提取的评估结果
图 8  模板匹配和标点过程的实验结果对比
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