顾及噪声密度函数差异的自适应回归算法及其在人工标靶提取中的应用

doi:10.11947/j.AGCS.2021.20190509

测绘学报 ›› 2021, Vol. 50 ›› Issue (2): 226-234.doi: 10.11947/j.AGCS.2021.20190509

顾及噪声密度函数差异的自适应回归算法及其在人工标靶提取中的应用

贾象阳, 黄先锋, 牛文渊, 张帆, 高云龙, 杨冲

武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079

收稿日期:2019-12-13 修回日期:2020-12-21 发布日期:2021-03-03
通讯作者: 黄先锋 E-mail:huangxf@whu.edu.cn
作者简介:贾象阳(1989-),男,博士,研究方向为三维激光点云处理与倾斜摄影测量。E-mail:jiaxiangyang@whu.edu.cn
基金资助:
国家重点研发计划（2020YFC1523003；2020YFC1522703）；国家自然科学基金（41571437）；深圳市空间信息智能感知与服务重点实验室开放基金

A self-adaptive regression algorithm with noise density function difference and its application to artificial target extraction

JIA Xiangyang, HUANG Xianfeng, NIU Wenyuan, ZHANG Fan, GAO Yunlong, YANG Chong

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Received:2019-12-13 Revised:2020-12-21 Published:2021-03-03
Supported by:
The National Key Research and Development Program of China (Nos. 2020YFC1523003;2020YFC1522703);The National Natural Science Foundation of China (No. 41571437);The Funded By Open Research Fund Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services

摘要/Abstract

摘要： 仪器、周围环境和人为操作等往往会造成点云中包含大量的噪声，导致模型回归精度低等问题。RANSAC算法凭借其简单实现、稳健的优势广泛应用于解决模型回归的问题。但是，针对不同的场景，RANSAC算法需要不断地调整参数来估计最优模型解。本文考虑到RANSAC及其现有改进算法的不足，以及内群点与噪声之间往往存在密度分布差异性，首先利用密度加权导向采样的方式优化初始假设模型，然后提出了一种空间密度函数以用于最优模型评价和迭代次数计算，整个过程不需要任何先验知识。本文方法能够解决内群点比率大于10%的模型回归问题。通过与已有方法的试验对比，本文方法能够在无先验信息的情况下具有较高的精度和稳健性。

关键词: 随机采样一致性, 加权采样, 空间密度函数, 密度差异性, 自适应阈值

Abstract: Instruments, surrounding environment and human operation often cause a lot of noise in the LiDAR, resulting in low model regression accuracy. RANSAC algorithm is widely used to solve model regression problems by virtue of its simple implementation and robustness. However, for different scenarios, RANSAC algorithm needs to constantly adjust the parameters to estimate the optimal model solution. Considering the RANSAC algorithm and its family existing shortcomings, according to the difference of density distribution between inliers and noise. This paper firstly optimizes the initial hypothesis model by using density weighted guided sampling, and then proposes a spatial density function to evaluate the optimal model and to calculate the number of iterations by using the spatial density function. The whole process does not need any prior knowledge. The method proposed in this paper can solve the model regression problem where the inliers ratio is more than 10%. In addition, compared with the existing methods, the method proposed in this paper can achieve high accuracy and robustness without prior information.

Key words: random sample consensus (RANSAC), weighted sample, space density function, density difference, self-adaptive threshold

中图分类号:

P237

贾象阳, 黄先锋, 牛文渊, 张帆, 高云龙, 杨冲. 顾及噪声密度函数差异的自适应回归算法及其在人工标靶提取中的应用[J]. 测绘学报, 2021, 50(2): 226-234.

JIA Xiangyang, HUANG Xianfeng, NIU Wenyuan, ZHANG Fan, GAO Yunlong, YANG Chong. A self-adaptive regression algorithm with noise density function difference and its application to artificial target extraction[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(2): 226-234.

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顾及噪声密度函数差异的自适应回归算法及其在人工标靶提取中的应用

A self-adaptive regression algorithm with noise density function difference and its application to artificial target extraction

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