Acta Geodaetica et Cartographica Sinica >

2017 , Vol. 46 >Issue 9: 1147 - 1155

DOI: https://doi.org/10.11947/j.AGCS.2017.20160606

Remote Sensing Image Change Detection Based on the Combination of Pixel-level and Object-level Analysis

  • FENG Wenqing ,
  • SUI Haigang ,
  • TU Jihui ,
  • SUN Kaimin
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  • State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Received date: 2016-11-28

  Revised date: 2017-05-24

  Online published: 2017-10-12

Supported by

The National Key Research and Development Program of China (No. 2016YFB0502600);The Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (No. 16E01);The National Natural Science Foundation of China(No. 41471354)

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Abstract

In order to improve the change detection accuracy of the high resolution remote sensing image, a novel framework based on the combination of pixel-level and object-level analysis is proposed. Firstly, the two temporal images are superimposed, and the principal component analysis is performed. Then, it is utilized that the entropy rate segmentation algorithm to segment the first principal component image by changing the number of super-pixels to obtain the multi-layer super-pixel regions with different sizes. At the same time, by analyzing the difference of spectral feature and texture feature on two temporal images, it is used that adaptive PCNN neural network algorithm to make a fusion of the two difference images. Afterwards, the level set (CV) method is used to get the pixel-level change detection results. At last, the change intensity level quantization and decision level fusion are used on the initial change detection results with the region labeling matrix, serving as the post-processing part to obtain the changed objects. Experimental results on the sets of SPOT-5 multi-spectral images show that the new framework can effectively integrate the advantages of pixel-based and object-based image analysis methods, which can further improve the stability and applicability of the change detection process.

Key words： pixel-level; object-level; change detection; super-pixel; PCNN neural network; decision level fusion

Cite this article

FENG Wenqing , SUI Haigang , TU Jihui , SUN Kaimin . Remote Sensing Image Change Detection Based on the Combination of Pixel-level and Object-level Analysis[J]. Acta Geodaetica et Cartographica Sinica, 2017 , 46(9) : 1147 -1155 . DOI: 10.11947/j.AGCS.2017.20160606

References

[1] JIA Lu, LI Ming, ZHANG Peng, et al. Remote-sensing Image Change Detection with Fusion of Multiple Wavelet Kernels[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(8):3405-3418.
[2] 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.
[3] 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):173696.
[4] 王琰, 舒宁, 龚龑. 利用马尔柯夫随机场图模型的变化像斑类别判定方法[J]. 武汉大学学报(信息科学版), 2012, 37(5):542-545. WANG Yan, SHU Ning, GONG Yan. Determination of New Class Properties of the Changed Image Segments Using MRF Graph Model[J]. Geomatics and Information Science of Wuhan University, 2012, 37(5):542-545.
[5] 佃袁勇, 方圣辉, 姚崇怀. 一种面向地理对象的遥感影像变化检测方法[J]. 武汉大学学报(信息科学版), 2014, 39(8):906-912. DIAN Yuanyong, FANG Shenghui, YAO Chonghuai. The Geographic Object-based Method for Change Detection with Remote Sensing Imagery[J]. Geomatics and Information Science of Wuhan University, 2014, 39(8):906-912.
[6] 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.
[7] CHEN Qiang, CHEN Yunhao.Multi-feature Object-based Change Detection Using Self-adaptive Weight Change Vector Analysis[J]. Remote Sensing, 2016, 8(7):549.
[8] 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.
[9] 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.
[10] 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):083678.
[11] 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.
[12] CAO Guo, ZHOU Licun, LI Yupeng. A New Change-detection Method in High-resolution Remote Sensing Images Based on A Conditional Random Field Model[J]. International Journal of Remote Sensing, 2016, 37(5):1173-1189.
[13] WU Chen, ZHANG Lefei, ZHANG Liangpei. A Scene Change Detection Framework for Multi-temporal Very High Resolution Remote Sensing Images[J]. Signal Processing,2016(124):184-197.
[14] 李杨, 江南, 侍昊, 等. LandSat-8影像的LDA模型变化检测[J]. 地球信息科学学报, 2015, 17(3):353-360. LI Yang, JIANG Nan, SHI Hao, et al. Change Detection and Analysis of LandSat-8 Image Based on LDA Model[J]. Journal of Geo-Information Science, 2015, 17(3):353-360.
[15] ZHANG Hui, GONG Maoguo, ZHANG Puzhao, et al. Feature-level Change Detection Using Deep Representation and Feature Change Analysis for Multispectral Imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(11):1666-1670.
[16] 戴芹, 刘建波, 刘士彬. 微粒群优化方法的遥感影像变化检测研究[J]. 测绘学报, 2012, 41(6):857-863, 885. DAI Qin, LIU Jianbo, LIU Shibin. Remote Sensing Image Change Detection Using Particle Swarm Optimization Algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(6):857-863, 885.
[17] ZHANG Puzhao, GONG Maoguo, SU Linzhi, et al. Change Detection Based on Deep Feature Representation and Mapping Transformation for Multi-spatial-resolution Remote Sensing Images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016(116):24-41.
[18] 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.
[19] LU Jun, LI J, CHEN Gang, et al. Improving Pixel-based Change Detection Accuracy Using an Object-based Approach in Multitemporal SAR Flood Images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(7):3786-3496.
[20] AGUIRRE-GUTIÉRREZ J, SEIJMONSBERGEN A C, DUIVENVOORDEN J F. Optimizing Land Cover Classification Accuracy for Change Detection, A Combined Pixel-based and Object-based Approach in A Mountainous Area in Mexico[J]. Applied Geography, 2012(34):29-37.
[21] 王东广, 肖鹏峰, 宋晓群, 等. 结合纹理信息的高分辨率遥感图像变化检测方法[J]. 国土资源遥感, 2012(4):76-81. WANG Dongguang, XIAO Pengfeng, SONG Xiaoqun, et al. Change Detection Method for High Resolution Remote Sensing Image in Association with Textural and Spectral Information[J]. Remote Sensing for Land & Resources, 2012(4):76-81.
[22] 彭钢, 贾振红, 覃锡忠, 等. 自适应PCNN和改进C-V结合的遥感图像变化检测[J]. 计算机工程与设计, 2015, 36(6):1581-1585. PENG Gang, JIA Zhenhong, QIN Xizhong, et al. Remote Sensing Image Change Detection Based on Adaptive PCNN and Improved C-V Model[J]. Computer Engineering and Design, 2015, 36(6):1581-1585.
[23] 李美丽, 李言俊, 王红梅, 等. 基于自适应脉冲耦合神经网络图像融合新算法[J]. 光电子·激光, 2010, 21(5):779-782. LI Meili, LI Yanjun, WANG Hongmei, et al. A New Image Fusion Algorithm Based on Adaptive PCNN[J]. Journal of Optoelectronics·Laser, 2010, 21(5):779-782.
[24] LIU Mingyu, TUZEL O, RAMALINGAMS S, et al. Entropy Rate Superpixel Segmentation[C]//Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC, USA:IEEE Computer Society, 2011:2097-2104.
[25] 殷瑞娟, 施润和, 李镜尧, 等. 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, et al. Automatic Selection of Optimal Segmentation Scale of High-resolution Remote Sensing Images[J]. Journal of Geo-Information Science, 2013, 15(6):902-910.
[26] 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.
[27] 吴俊政, 严卫东, 倪维平, 等. 基于图像融合与多尺度分割的目标级变化检测[J]. 电光与控制, 2013, 20(12):51-55.
WU Junzheng, YAN Weidong, NI Weiping, et al. Object-level Change Detection Based on Image Fusion and Multi-scale Segmentation[J]. Electronics Optics & Control, 2013, 20(12):51-55.
[28] 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.AGCS2.0152.0140260.
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.AGCS2.0152.0140260.
[29] LI Chunming, XU Chenyang, GUI Changfeng, et al. Distance Regularized Level Set Evolution and Its Application to Image Segmentation[J]. IEEE Transactions on Image Processing, 2010, 19(12):3243-3254.
[30] SUN Kaimin, CHEN Yan. The Application of Objects Change Vector Analysis in Object-level Change Detection[C]. International Conference on Computational Intelligence and Industrial Application (PACⅡA), 2010, 15(4):383-389.
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