Acta Geodaetica et Cartographica Sinica >

2018 , Vol. 47 >Issue 1: 102 - 112

DOI: https://doi.org/10.11947/j.AGCS.2018.20170483

Combining the Pixel-based and Object-based Methods for Building Change Detection Using High-resolution Remote Sensing Images

  • ZHANG Zhiqiang ,
  • ZHANG Xinchang ,
  • XIN Qinchuan ,
  • YANG Xiaoling
Expand
  • 1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
    2. Guangzhou University, Guangzhou 510006, China;
    3. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Guangzhou 510275, China;
    4. Guangdong Urban & Rural Planning and Design Institute, Guangzhou 510290, China

Received date: 2017-08-30

  Revised date: 2017-11-17

  Online published: 2018-02-05

Supported by

The National Natural Science Foundation of China (Nos. 41431178;41671453);The National Natural Science Foundation of Guangdong Province of China (No. 2016A030311016);Key Projects for Young Teachers at Sun Yat-sen University (No. 17lgzd02);The National Administration of Surveying,Mapping and Geoinformation of China (No. GZIT2016-A5-147)

Fold

Abstract

Timely and accurate change detection of buildings provides important information for urban planning and management.Accompanying with the rapid development of satellite remote sensing technology,detecting building changes from high-resolution remote sensing images have received wide attention.Given that pixel-based methods of change detection often lead to low accuracy while object-based methods are complicated for uses,this research proposes a method that combines pixel-based and object-based methods for detecting building changes from high-resolution remote sensing images.First,based on the multiple features extracted from the high-resolution images,a random forest classifier is applied to detect changed building at the pixel level.Then,a segmentation method is applied to segement the post-phase remote sensing image and to get post-phase image objects.Finally,both changed building at the pixel level and post-phase image objects are fused to recognize the changed building objects.Multi-temporal QuickBird images are used as experiment data for building change detection with high-resolution remote sensing images,the results indicate that the proposed method could reduce the influence of environmental difference,such as light intensity and view angle,on building change detection,and effectively improve the accuracies of building change detection.

Key words： building change detection; information fusion; random forest; image segmentation; high resolution

Cite this article

ZHANG Zhiqiang , ZHANG Xinchang , XIN Qinchuan , YANG Xiaoling . Combining the Pixel-based and Object-based Methods for Building Change Detection Using High-resolution Remote Sensing Images[J]. Acta Geodaetica et Cartographica Sinica, 2018 , 47(1) : 102 -112 . DOI: 10.11947/j.AGCS.2018.20170483

References

[1] HUANG Xin, ZHU Tingting, ZHANG Liangpei, et al. A Novel Building Change Index for Automatic Building Change Detection from High-Resolution Remote Sensing Imagery[J]. Remote Sensing Letters, 2014, 5(8): 713-722.
[2] MATIKAINEN L, HYYPP J, AHOKAS E, et al. Automatic Detection of Buildings and Changes in Buildings for Updating of Maps[J]. Remote Sensing, 2010, 2(5): 1217-1248.
[3] 张永梅, 李立鹏, 姜明, 等. 综合像素级和特征级的建筑物变化检测方法[J]. 计算机科学, 2013, 40(1): 286-293. ZHANG Yongmei, LI Lipeng, JIANG Ming, et al. Change Detection Method for Buildings Based on Pixel-level and Feature-Level[J]. Computer Science, 2013, 40(1): 286-293.
[4] XIAO Pengfeng, ZHANG Xueliang, WANG Dongguang, et al. Change Detection of Built-Up Land: A Framework of Combining Pixel-Based Detection and Object-Based Recognition[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 119: 402-414.
[5] HUSSAIN M, CHEN Dongmei, CHENG A, et al. Change Detection from Remotely Sensed Images: From Pixel-Based to Object-Based Approaches[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 80: 91-106.
[6] XIN Qinchuan, OLOFSSON P, ZHU Zhe, et al. Toward Near Real-Time Monitoring of Forest Disturbance by Fusion of MODIS and Landsat Data[J]. Remote Sensing of Environment, 2013, 135: 234-247.
[7] LU D, MAUSEL P, BRONDÍZIO E, et al. Change Detection Techniques[J]. International Journal of Remote Sensing, 2004, 25(12): 2365-2401.
[8] PRAKASH A, GUPTA R P. Land-Use Mapping and Change Detection in a Coal Mining Area-A Case Study in the Jharia Coalfield, India[J]. International Journal of Remote Sensing, 1998, 19(3): 391-410.
[9] JOHNSON R D, KASISCHKE E S. Change Vector Analysis: A Technique for the Multispectral Monitoring of Land cover and Condition[J]. International Journal of Remote Sensing, 1998, 19(3): 411-426.
[10] WARD D, PHINN S R, MURRAY A T. Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data[J]. The Professional Geographer, 2000, 52(3): 371-386.
[11] CHEN Gang, HAY G J, CARVALHO L M T, et al. Object-Based Change Detection[J]. International Journal of Remote Sensing, 2012, 33(14): 4434-4457.
[12] JOHANSEN K, ARROYO L A, PHINN S, et al. Comparison of Geo-Object-Based and Pixel-Based Change Detection of Riparian Environments Using High Spatial Resolution Multi-Spectral Imagery[J]. Photogrammetric Engineering and Remote Sensing, 2010, 76(2): 123-136.
[13] 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, 959. 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, 959. DOI: 10.13485/j.cnki.11-2089.2014.0138.
[14] CHEN Jianyu, MAO Zhihua, PHILPOT B, et al. Detecting Changes in High-Resolution Satellite Coastal Imagery Using An Image Object Detection Approach[J]. International Journal of Remote Sensing, 2013, 34(7): 2454-2469.
[15] 佃袁勇, 方圣辉, 姚崇怀. 多尺度分割的高分辨率遥感影像变化检测[J]. 遥感学报, 2016, 20(1): 129-137. DIAN Yuanyong, FANG Shenghui, YAO Chonghuai. Change Detection for High-Resolution Images Using Multilevel Segment Method[J]. Journal of Remote Sensing, 2016, 20(1): 129-137.
[16] CAO Guo, LI Yupeng, LIU Yazhou, et al. Automatic Change Detection in High-Resolution Remote-Sensing Images by Means of Level Set Evolution and Support vector Machine Classification[J]. International Journal of Remote Sensing, 2014, 35(16): 6255-6270.
[17] 杜培军, 柳思聪. 融合多特征的遥感影像变化检测[J]. 遥感学报, 2012, 16(4): 663-677. DU Peijun, LIU Sicong. Change Detection From Multi-temporal Remote Sensing Images by Integrating Multiple Features[J]. Journal of Remote Sensing, 2012, 16(4): 663-677.
[18] 兰泽英, 刘洋. 领域知识辅助下基于多尺度与主方向纹理的遥感影像土地利用分类[J]. 测绘学报, 2016, 45(8): 973-982. DOI: 10.11947/j.AGCS.2016.20150624. LAN Zeying, LIU Yang. Classification of Land-use Based on Remote Sensing Image Texture Features with Multi-scales and Cardinal Direction Inspired by Domain Knowledge[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(8): 973-982. DOI: 10.11947/j.AGCS.2016.20150624.
[19] VOLPI M, TUIA D, BOVOLO F, et al. Supervised Change Detection in VHR Images Using Contextual Information and Support Vector Machines[J]. International Journal of Applied Earth Observation and Geoinformation, 2013, 20: 77-85.
[20] HUANG Xin, ZHANG Liangpei, ZHU Tingting. Building Change Detection From Multitemporal High-Resolution Remotely Sensed Images Based on A Morphological Building Index[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 105-115.
[21] 胡荣明, 黄小兵, 黄远程. 增强形态学建筑物指数应用于高分辨率遥感影像中建筑物提取[J]. 测绘学报, 2014, 43(5): 514-520. DOI: 10.13485/j.cnki.11-2089.2014.0084. HU Rongming, HUANG Xiaobing, HUANG Yuancheng. An Enhanced Morphological Building Index for Building Extraction From High-Resolution Images[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(5): 514-520. DOI: 10.13485/j.cnki.11-2089.2014.0084.
[22] RODRIGUEZ-GALIANO V F, GHIMIRE B, ROGAN J, et al. An Assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 67: 93-104.
[23] BREIMAN L. Random Forests[J]. Machine Learning, 2001, 45(1): 5-32.
[24] 巫兆聪, 胡忠文, 张谦, 等. 结合光谱、纹理与形状结构信息的遥感影像分割方法[J]. 测绘学报, 2013, 42(1): 44-50. WU Zhaocong, HU Zhongwen, ZHANG Qian, et al. On Combining Spectral, Textural and Shape Features for Remote Sensing Image Segmentation[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(1): 44-50.
[25] HU Zhongwen, ZOU Qin, LI Qingquan. Watershed Superpixel[C]//Proceedings of 2015 IEEE International Conference on Image Processing. Quebec City, QC, Canada: IEEE, 2015: 349-353.
[26] HARIS K, EFSTRATIADIS S N, MAGLAVERAS N, et al. Hybrid Image Segmentation Using Watersheds and Fast Region Merging[J]. IEEE Transactions on Image Processing, 1998, 7(12): 1684-1699.
[27] 陈杰, 邓敏, 肖鹏峰, 等. 利用小波变换的高分辨率多光谱遥感图像多尺度分水岭分割[J]. 遥感学报, 2011, 15(5): 908-926. CHEN Jie, DENG Min, XIAO Pengfeng, et al. Multi-scale Watershed Segmentation of High-Resolution Multi-spectral Remote Sensing Image Using Wavelet Transform[J]. Journal of Remote Sensing, 2011, 15(5): 908-926.
[28] HERMOSILLA T, RUIZ L A, RECIO J A, et al. Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data[J]. Remote Sensing, 2011, 3(6): 1188-1210.
[29] 林祥国, 宁晓刚. 融合直角点和直角边特征的高分辨率遥感影像居民点提取方法[J]. 测绘学报, 2017, 46(1): 83-89. DOI: 10.11947/j.AGCS.2017.20160389. LIN Xiangguo, NING Xiaogang. Extraction of Human Settlements from High Resolution Remote Sensing Imagery by Fusing Features of Right Angle Corners and Right Angle Sides[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(1): 83-89. DOI: 10.11947/j.AGCS.2017.20160389.
Options
Outlines

/

〈 〉