高分辨率遥感影像的随机森林变化检测方法

doi:10.11947/j.AGCS.2017.20170074

测绘学报 ›› 2017, Vol. 46 ›› Issue (11): 1880-1890.doi: 10.11947/j.AGCS.2017.20170074

高分辨率遥感影像的随机森林变化检测方法

冯文卿¹, 眭海刚¹, 涂继辉^1,2, 孙开敏¹, 黄伟明³

1. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079;
2. 长江大学电子信息学院, 湖北荆州 434023;
3. 隆德大学自然地理和生态系统科学系, 瑞典隆德 22362

收稿日期:2017-03-01 修回日期:2017-09-09 出版日期:2017-11-20 发布日期:2017-12-05
通讯作者: 眭海刚 E-mail:haigang_sui@263.net
作者简介:冯文卿(1991-),男,博士生,研究方向为高分辨率遥感影像分类及变化检测。E-mail:wq_feng@whu.edu.cn
基金资助:
国家重点研发计划（2016YFB0502603）；测绘遥感信息工程国家重点实验室开放基金（16E01）；国家自然科学基金（41471354）

Change Detection Method for High Resolution Remote Sensing Images Using Random Forest

FENG Wenqing¹, SUI Haigang¹, TU Jihui^1,2, SUN Kaimin¹, HUANG Weiming³

1. State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2. Electronics & Information School of Yangtze University, Jingzhou 434023, China;
3. Department of Physical Geography and Ecosystem Science, Lund University, Lund 22362, Sweden

Received:2017-03-01 Revised:2017-09-09 Online:2017-11-20 Published:2017-12-05
Supported by:
The National Key Research and Development Program of China (No. 2016YFB0502603) Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (No. 16E01) The National Natural Foundation of China (NSFC)(No. 41471354)

摘要/Abstract

摘要：

基于面向对象分析（OBIA）的遥感影像变化检测研究已取得显著的进展，代表了遥感影像变化检测的发展范式，未来是发展更加智能的解译分析方法。随机森林作为一种新的机器学习算法，其预测效果和性能稳定性要优于许多单预测器和集成预测方法。本文充分利用OBIA及随机森林机器学习算法的优势，提出了利用随机森林进行面向对象的遥感影像变化检测。首先基于熵率对影像进行超像素分割，通过最优超像素个数评价指数来获取最佳的影像分割结果，并提取每个超像素在前、后时相影像上的光谱特征和Gabor特征作为随机森林的特征输入数据，用于模型的训练。在初始像素级检测结果之上，自动进行分类样本选择并构建分类器模型，用训练好的模型来提取最终的变化区域。利用Quickbird、IKONOS、SPOT-5等3组多光谱影像进行试验，结果表明，本文方法在变化检测精度上要优于对比方法。

关键词: 分割, 超像素, 特征, 随机森林, 变化检测

Abstract:

Studies based on object-based image analysis (OBIA) representing the paradigm shift in remote sensing image change detection (CD) have achieved remarkable progress in the last decade.Their aim has been developing more intelligent interpretation analysis methods in the future.The prediction effect and performance stability of random forest (RF),as a new kind of machine learning algorithm,are better than many single predictors and integrated forecasting method. This paper presents a novel RF OBIA method for high resolution remote sensing image CD that makes full use of the advantages of RF and OBIA. Firstly,the entropy rate segmentation algorithm is used to segment the image for the purpose of measuring the homogeneity of super-pixels. Then the optimal image segmentation result is obtained from the evaluation index of the optimal super-pixel number.Afterwards,the spectral features and Gabor features of each super-pixelareextracted and used as feature datasets for the training of RF model.On the basis of the initial pixel-level CD result,the changed and unchanged samples are automatically selected and used to build the classifier model in order to get the final object-level CD result.Experimental results on Quickbird,IKONOS and SPOT-5 multi-spectral images show that the proposed method out performs the compared methods in the accuracy of CD.

Key words: segmentation, super-pixel, feature, random forest, change detection

中图分类号:

P237

冯文卿, 眭海刚, 涂继辉, 孙开敏, 黄伟明. 高分辨率遥感影像的随机森林变化检测方法[J]. 测绘学报, 2017, 46(11): 1880-1890.

FENG Wenqing, SUI Haigang, TU Jihui, SUN Kaimin, HUANG Weiming. Change Detection Method for High Resolution Remote Sensing Images Using Random Forest[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(11): 1880-1890.

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高分辨率遥感影像的随机森林变化检测方法

Change Detection Method for High Resolution Remote Sensing Images Using Random Forest

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