高分辨率遥感影像的随机森林变化检测方法

冯文卿; 眭海刚; 涂继辉; 孙开敏; 黄伟明

doi:10.11947/j.AGCS.2017.20170074

测绘学报 >

2017 , Vol. 46 >Issue 11: 1880 - 1890

DOI: https://doi.org/10.11947/j.AGCS.2017.20170074

摄影测量学与遥感

高分辨率遥感影像的随机森林变化检测方法

冯文卿 ,
眭海刚 ,
涂继辉 ,
孙开敏 ,
黄伟明

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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

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

收稿日期: 2017-03-01

修回日期: 2017-09-09

网络出版日期: 2017-12-05

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）

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

FENG Wenqing ,
SUI Haigang ,
TU Jihui ,
SUN Kaimin ,
HUANG Weiming

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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 date: 2017-03-01

Revised date: 2017-09-09

Online 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)

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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

本文引用格式

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 . DOI: 10.11947/j.AGCS.2017.20170074

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

参考文献

[1] 李亮,舒宁,王凯,等.融合多特征的遥感影像变化检测方法[J].测绘学报,2014,43(9):945-953.DOI:10.13485/j.cnki.11-2089.2014.0138. LI Liang,SHU Ning,WANG Kai,et al.Change Detection Method for Remote Sensing Images Based on Multi-features Fusion[J].Acta Geodaetica et Cartographica Sinica,2014,43(9):945-953.DOI:10.13485/j.cnki.11-2089.2014.0138.
[2] DURO D C,FRANKLIN S E,DUBÉ M G.A Comparison of Pixel-based and Object-based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery[J].Remote Sensing of Environment,2012,118(2):259-272.
[3] HAZEL G G.Object-level Change Detection in Spectral Imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(3):553-561.
[4] WANG Chao,XU Mengxi,WANG Xin,et al.Object-Oriented Change Detection Approach for High-resolution Remote Sensing Images Based on Multiscale Fusion[J].Journal of Applied Remote Sensing,2013,7(1):73-82.
[5] HAO Ming,ZHANG Hua,SHI Wenzhong,et al.Unsupervised Change Detection Using Fuzzy c-means and MRF from Remotely Sensed Images[J].Remote Sensing Letters,2013,4(12):1185-1194.
[6] SHAO Pan,SHI Wenzhong,HE Pengfei,et al.Novel Approach to Unsupervised Change Detection Based on a Robust Semi-supervised FCM Clustering Algorithm[J].Remote Sensing,2016,8(3):264.
[7] CAO Guo,LIU Yazhou,SHANG Yanfeng.Automatic Change Detection in Remote Sensing Images Using Level Set Method with Neighborhood Constraints[J].Journal of Applied Remote Sensing,2014,8(1):83-89.
[8] ZHOU Licun,CAO Guo,LI Yupeng,et al.Change Detection Based on Conditional Random Field with Region Connection Constraints in High-resolution Remote Sensing Images[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(8):3478-3488.
[9] HAO Ming,SHI Wenzhong,ZHANG Hua,et al.Unsupervised Change Detection with Expectation-maximization-based Level Set[J].IEEE Geoscience and Remote Sensing Letters,2014,11(1):210-214.
[10] CELIK T.Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-means Clustering[J].IEEE Geoscience and Remote Sensing Letters,2009,6(4):772-776.
[11] WANG Wenjie,ZHAO Zhongming,ZHU Haiqing.Object-oriented Change Detection Method Based on Multi-scale and Multi-Feature Fusion[C]//2009 Joint Urban Remote Sensing Event.Shanghai:IEEE,2009:1-5.
[12] SUN Kaimin,CHEN Yan.The Application of Objects Change Vector Analysis in Object-level Change Detection[C].Proceedings of the International Conference on Computational Intelligence and Industrial Application (PACⅡA).[S.l.:s.n.],2010,15(4):383-389.
[13] 王琰,舒宁,龚龑.高分辨率遥感影像土地利用变化检测方法研究[J].国土资源遥感,2012,24(1):43-47. WANG Yan,SHU Ning,GONG Yan.A Study of Land Use Change Detection Based on High Resolution Remote Sensing Images[J].Remote Sensing for Land & Resources,2012,24(1):43-47.
[14] 冯文卿,张永军.利用多尺度融合进行面向对象的遥感影像变化检测[J].测绘学报,2015,44(10):1142-1151.DOI:10.11947/j.AGCS.2015.20140260. FENG Wenqing,ZHANG Yongjun.Object-oriented Change Detection for Remote Sensing Images Based on Multi-scale Fusion[J].Acta Geodaetica et Cartographica Sinica,2015,44(10):1142-1151.DOI:10.11947/j.AGCS.2015.20140260.
[15] HAO Ming,SHI Wenzhong,ZHANG Hua,et al.A Scale-driven Change Detection Method Incorporating Uncertainty Analysis for Remote Sensing Images[J].Remote Sensing,2016,8(9):745.
[16] ZHANG Junping,MU Chunfang,CHEN Hao,et al.Multi-scale Segmentation in Change Detection for Urban High Resolution Images[C]//Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium.Vancouver,BC:IEEE,2011:209-212.
[17] TANG Yuqi,ZHANG Liangpei,HUANG Xin.Object-oriented Change Detection Based on the Kolmogorov-Smirnov Test Using High-resolution Multispectral Imagery[J].International Journal of Remote Sensing,2011,32(20):5719-5740.
[18] 顾海燕,闫利,李海涛,等.基于随机森林的地理要素面向对象自动解译方法[J].武汉大学学报(信息科学版),2016,41(2):228-234. GU Haiyan,YAN Li,LI Haitao,et al.An Object-based Automatic Interpretation Method for Geographic Features Based on Random Forest Machine Learning[J].Geomatics and Information Science of Wuhan University,2016,41(2):228-234.
[19] DAUGMAN J G.Complete Discrete 2-D Gabor Transforms by Neural Networks for Image Analysis and Compression[J].IEEE Transactions on Acoustics,Speech,and Signal Processing,1988,36(7):1169-1179.
[20] 陈洪,陶超,邹峥嵘,等.一种新的高分辨率遥感影像城区提取方法[J].武汉大学学报(信息科学版),2013,38(9):1063-1067. CHEN Hong,TAO Chao,ZOU Zhengrong,et al.Automatic Urban Area Extraction Using A Gabor Filter and High Resolution Remote Sensing Imagery[J].Geomatics and Information Science of Wuhan University,2013,38(9):1063-1067.
[21] LIU Mingyu,TUZEL O,RAMALINGAM S,et al.Entropy Rate Superpixel Segmentation[C]//Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition.Providence,RI:IEEE,2011:2097-2104.
[22] 殷瑞娟,施润和,李镜尧.一种高分辨率遥感影像的最优分割尺度自动选取方法[J].地球信息科学学报,2013,15(6):902-910. YIN Ruijuan,SHI Runhe,LI Jingyao.Automatic Selection of Optimal Segmentation Scale of High-resolution Remote Sensing Images[J].Journal of Geo-Information Science,2013,15(6):902-910.
[23] ESPINDOLA G M,CAMARA G,REIS I A,et al.Parameter Selection for Region-growing Image Segmentation Algorithms Using Spatial Autocorrelation[J].International Journal of Remote Sensing,2006,27(14):3035-3040.
[24] 郭天宝,池天河,彭玲,等.利用随机森林的高分一号遥感数据进行城市用地分类[J].测绘通报,2016(5):73-76.DOI:10.13474/j.cnki.11-2246.2016.0159. GUO Tianbao,CHI Tianhe,PENG Ling,et al.Classification of GF-1 Remote Sensing Image Based on Random Forests for Urban Land-use[J].Bulletin of Surveying and Mapping,2016(5):73-76.DOI:10.13474/j.cnki.11-2246.2016.0159.
[25] 杜培军,柳思聪,郑辉.基于支持向量机的矿区土地覆盖变化检测[J].中国矿业大学学报,2012,41(2):262-267. DU Peijun,LIU Sicong,ZHENG Hui.Land Cover Change Detection over Mining Areas Based on Support Vector Machine[J].Journal of China University of Mining & Technology,2012,41(2):262-267.

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