基于随机森林的地理要素面向对象自动解译方法

顾海燕; 闫利; 李海涛; 贾莹

doi:10.13203/j.whugis20140102

基于随机森林的地理要素面向对象自动解译方法

doi: 10.13203/j.whugis20140102

1.
武汉大学测绘学院, 湖北武汉, 430079;
2.
中国测绘科学研究院, 北京, 100830;
3.
高德软件有限公司, 北京, 100080

基金项目: 国家科技支撑计划(2012BAH28B03);地理空间信息工程国家测绘地理信息局重点实验室开放基金(201101)。

详细信息

作者简介:
顾海燕,博士生,主要从事遥感影像智能解译方法与应用研究。guhy@casm.ac.cn

中图分类号: P208;P237

An Object-based Automatic Interpretation Method for Geographic Features Based on Random Forest Machine Learning

1.
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;
2.
Key Laboratory of Geo-informatics of State Bureau of Surveying and Mapping, Chinese Academy of Surveying and Mapping, Beijing 100830, China;
3.
AutoNavi Software Co. Ltd., Beijing 100080, China

Funds: The National Science & Technology Pillar Program, No. 2012BAH28B03; Key Laboratory of Geoinformatics of National Administration of Surveying, Mapping and Geoinformation, No.201101.

摘要: 面向地理对象影像分析(GEOBIA)技术取得了显著的进展,代表了遥感影像解译的发展范式,其主要目标是发展智能化分析方法。随机森林机器学习方法是一种相对新的、数据驱动的非参数分类方法,具有自动特征优选、自动模型构建等优势,为智能化分析提供了有效手段。充分利用GEOBIA及随机森林机器学习的优势,提出了基于随机森林的地理要素面向对象自动解译方法,阐述了随机森林面向对象分类方法的技术流程,为设计和实现该方法提供了详细指导,有助于指导用户优选特征和构建分类模型。通过与支持向量机分类的对比实验证明,该方法可以自动进行特征优选及分类模型的构建,利用较少的特征得到较高的分类精度,在不损失性能的前提下减少了计算量和内存使用,能够为大范围、大区域地理要素自动解译提供先验知识及自动化的手段。
- 面向地理对象影像分析 /
- 随机森林 /
- 分类模型 /
- 特征选择
Abstract: Geographic object-based image analysis (GEOBIA) techniques have recently seen considerable development in comparison to traditional pixel-based image analysis, representing a paradigm shift in remote sensing interpretation. The main aim is to incorporate and develop geographic-based intelligence. The random forest (RF) machine learning method is a relatively new, non-parametric, data-driven classification method that can supply intelligent means for feature selection and classification modelling. This paper presents a novel RF GEOBIA method for land-cover classification that makes full use of the advantages of GEOBIA and RF. A detailed RF GEOBIA workflow is proposed to guide the design and implementation of the method, and to guide experts during elaboration of feature selection and classification modelling. Theoretical and experimental results are compared with the support vector machine (SVM) approach, demonstrating that it is a robust and intelligent method for land-cover classification with wrapper feature selection and classification modelling. The RF GEOBIA method reduces the number of features required, computing time, and memory requirements, with no associated reduction in performance. It also provides a priori knowledge for further classification and supports large scale applications where "big data" is involved.
- geographic object-based image analysis (GEOBIA) /
- random forest /
- classification model /
- feature selection

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基于随机森林的地理要素面向对象自动解译方法

doi: 10.13203/j.whugis20140102

作者简介:
顾海燕,博士生,主要从事遥感影像智能解译方法与应用研究。guhy@casm.ac.cn

An Object-based Automatic Interpretation Method for Geographic Features Based on Random Forest Machine Learning

计量

基于随机森林的地理要素面向对象自动解译方法

doi: 10.13203/j.whugis20140102

1. 武汉大学测绘学院, 湖北武汉, 430079;

2. 中国测绘科学研究院, 北京, 100830;

3. 高德软件有限公司, 北京, 100080

作者简介:
顾海燕,博士生,主要从事遥感影像智能解译方法与应用研究。guhy@casm.ac.cn

English Abstract

An Object-based Automatic Interpretation Method for Geographic Features Based on Random Forest Machine Learning

1. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;

2. Key Laboratory of Geo-informatics of State Bureau of Surveying and Mapping, Chinese Academy of Surveying and Mapping, Beijing 100830, China;

3. AutoNavi Software Co. Ltd., Beijing 100080, China

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留言板

基于随机森林的地理要素面向对象自动解译方法

doi: 10.13203/j.whugis20140102

作者简介: 顾海燕,博士生,主要从事遥感影像智能解译方法与应用研究。guhy@casm.ac.cn

An Object-based Automatic Interpretation Method for Geographic Features Based on Random Forest Machine Learning

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出版历程

基于随机森林的地理要素面向对象自动解译方法

doi: 10.13203/j.whugis20140102

1. 武汉大学测绘学院, 湖北武汉, 430079; 2. 中国测绘科学研究院, 北京, 100830; 3. 高德软件有限公司, 北京, 100080

作者简介: 顾海燕,博士生,主要从事遥感影像智能解译方法与应用研究。guhy@casm.ac.cn

English Abstract

An Object-based Automatic Interpretation Method for Geographic Features Based on Random Forest Machine Learning

1. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China; 2. Key Laboratory of Geo-informatics of State Bureau of Surveying and Mapping, Chinese Academy of Surveying and Mapping, Beijing 100830, China; 3. AutoNavi Software Co. Ltd., Beijing 100080, China

全文HTML

目录

作者简介:
顾海燕,博士生,主要从事遥感影像智能解译方法与应用研究。guhy@casm.ac.cn

1. 武汉大学测绘学院, 湖北武汉, 430079;

2. 中国测绘科学研究院, 北京, 100830;

3. 高德软件有限公司, 北京, 100080

作者简介:
顾海燕,博士生,主要从事遥感影像智能解译方法与应用研究。guhy@casm.ac.cn

1. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;

2. Key Laboratory of Geo-informatics of State Bureau of Surveying and Mapping, Chinese Academy of Surveying and Mapping, Beijing 100830, China;

3. AutoNavi Software Co. Ltd., Beijing 100080, China