Unsupervised change detection of man-made objects using coherent and incoherent features of multi-temporal SAR images

doi:10.23919/JSEE.2022.000087

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (4): 896-906.doi: 10.23919/JSEE.2022.000087

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

Unsupervised change detection of man-made objects using coherent and incoherent features of multi-temporal SAR images

Hao FENG¹(), Jianzhong WU^2,^3,⁴(), Lu ZHANG^1,*(), Mingsheng LIAO¹()

¹ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
² Key Laboratory of Land Subsidence Monitoring and Prevention, Ministry of Land and Resources, Shanghai 200072, China
³ Shanghai Engineering Research Center of Land Subsidence, Shanghai 200072, China
⁴ Shanghai Institute of Geological Survey, Shanghai 200072, China

Received:2021-12-03 Online:2022-08-30 Published:2022-08-30
Contact: Lu ZHANG E-mail:fengh@whu.edu.cn;wjzhongsh@163.com;luzhang@whu.edu.cn;liao@whu.edu.cn
About author:|FENG Hao was born in 1992. He received his B.S. degree in geographic information system from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2014, and M.E. degree in surveying and mapping engineering from Wuhan University, Wuhan, China, in 2016. He is currently pursuing his Ph.D. degree in photogrammetry and remote sensing with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China. His research interests include multi-temporal synthetic aperture radar images processing and change detection. E-mail: fengh@whu.edu.cn||WU Jianzhong was born in 1977. He received his B.E. degree in hydrogeology and engineering geology from Chang’an University, Xi’an, China, in 1999, and M.E. degree in geological engineering from China University of Geosciences, Wuhan, China, in 2002. He has been with the Shanghai Institute of Geological Survey, Shanghai, where he became a senior engineer in 2011. Since 2016, he has been with the Key Laboratory of Land Subsidence Monitoring and Prevention, the Ministry of Nature Resources. His research interests include land subsidence monitoring and prevention technology.E-mail: wjzhongsh@163.com||ZHANG Lu was born in 1975. He received his B.E. and M.E. degrees in computer science and technology from Wuhan University of Hydraulic and Electrical Engineering, Wuhan, China, in 1997 and 2000, respectively, and Ph.D. degree in photogrammetry and remote sensing from Wuhan University, Wuhan, in 2005. From 2005 to 2007, he was a post-doctoral research fellow at the Institute of Space and Earth Information Science, Chinese University of Hong Kong, Hong Kong. Since 2007, he has been with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, where he became a full professor in 2013. He has been involved in several research projects funded by the National Natural Science Foundation of China and the Ministry of Science and Technology. He has authored about 30 peer-reviewed scientific papers. His research interests include synthetic aperture radar interferometry as well as remote sensing classification and change detection. E-mail: luzhang@whu.edu.cn||LIAO Mingsheng was born in 1962. He received his B.S. degree in electronic engineering from Wuhan Technical University of Surveying and Mapping (WTUSM), Wuhan, China, in 1982, M.A. degree in electronic and information engineering from Huazhong University of Science and Technology, Wuhan, in 1985, and Ph.D. degree in photogrammetry and remote sensing from WTUSM, in 2000. He has been with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, where he became a professor in 1997. He is the Principal Investigator (PI) of several projects funded by the Ministry of Science and Technology (MOST) China and the Natural Science Foundation of China. He is also the Co-PI of the ESA-MOST Cooperative Dragon I during 2004–2008, II during 2008–2012, III during 2012–2016, and IV during 2016–2020 projects. He has authored over 60 peer-reviewed journal papers and several book chapters focused on synthetic aperture radar interferometry techniques and applications. His research interests include remote sensing image processing and analysis, algorithms for interferometric synthetic aperture radar, integration and fusion of multisource spatial information, and applications of remote sensing data. E-mail: liao@whu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (41774006), the Comparative Study of Geo-environment and Geohazards in the Yangtze River Delta and the Red River Delta Project, and the Shanghai Science and Technology Development Foundation (20dz1201200).

Abstract

Abstract:

Constrained by complex imaging mechanism and extraordinary visual appearance, change detection with synthetic aperture radar (SAR) images has been a difficult research topic, especially in urban areas. Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information, there are still two problems to be solved in practical applications. First, change indicators constructed from incoherent feature only cannot characterize the change objects accurately. Second, the results of pixel-level methods are usually presented in the form of the noisy binary map, making the spatial change not intuitive and the temporal change of a single pixel meaningless. In this study, we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images. The coefficients of variation in time-series incoherent features and the man-made object index (MOI) defined with coherent features are first combined to identify the initial change pixels. Afterwards, an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise (DBSCAN) and dynamic time warping (DTW), which can transform the initial results into noiseless object-level patches, and take the cluster center as a representative of the man-made object to determine the change pattern of each patch. An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.

Key words: change detection, multi-temporal synthetic aperture radar (SAR) data, coherent and incoherent features, clustering

Hao FENG, Jianzhong WU, Lu ZHANG, Mingsheng LIAO. Unsupervised change detection of man-made objects using coherent and incoherent features of multi-temporal SAR images[J]. Journal of Systems Engineering and Electronics, 2022, 33(4): 896-906.

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Parameter	TerraSAR-X dataset
Acquisition date (YYYYMMDD)	20150216 20150504
	20150606 20150709
	20150811 20151016
	20151118 20151210
	20160101 20160203
Imaging mode	Stripmap
Polarization	VV
Pass direction	Descending
Incident angle/(°)	41.06
Slant range spacing/m	1.36
Azimuth spacing/m	1.89

Parameter	Value
Window size of enhanced-Lee filtering	3×3
Window size of coherence estimation	3×3
Threshold of change feature	0.3
Threshold of MOI	0.6
(?, MinPts)	(20,40)
Threshold of iterative convergence	0.25

Method	PR	ACC	RE	KAPPA
Proposed method	0.905	0.815	0.864	0.675
SFTC	0.829	0.611	0.659	0.593

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Unsupervised change detection of man-made objects using coherent and incoherent features of multi-temporal SAR images

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