适用于自然地表形变反演的小基线集方法

doi:10.12082/dqxxkx.2018.170579

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (4): 440-451.doi: 10.12082/dqxxkx.2018.170579

所属专题：气候变化与地表过程

• 全国激光雷达大会特约稿件 • 上一篇下一篇

适用于自然地表形变反演的小基线集方法

黄俊松¹(), 曾琪明^1,^*(), 高胜^1,², 焦健¹, 胡乐银³

1. 北京大学遥感与地理信息系统研究所,北京 100871
2. 中国科学院电子学研究所,北京 100190
3. 北京市地震局,北京 100080

收稿日期:2017-12-04 修回日期:2018-02-05 出版日期:2018-04-20 发布日期:2018-04-20
作者简介:
作者简介：黄俊松(1990-),男,博士生,研究方向为InSAR时序分析。E-mail: junsongh@pku.edu.cn
基金资助:
国家重点研发计划（2017YFB0502703）;内蒙古自治区科技厅“数字化矿区资源管理与矿区生态环境监测技术与应用”项目（2015-2019）

An Improved Small Baseline Subset Method for Deformation Retrieval of Natural Terrains

HUANG Junsong¹(), ZENG Qiming^1,^*(), GAO Sheng^1,², JIAO Jian¹, HU Leyin³

1. Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China
2. Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
3. Beijing Earthquake Agency, Beijing 100080, China

Received:2017-12-04 Revised:2018-02-05 Online:2018-04-20 Published:2018-04-20
Contact: ZENG Qiming
Supported by:
National Key R&D Program of China, No.2017YFB0502703;Digital Mining Resource Management and Mining Ecological Environment Monitoring Technology and Application Project of the Science and Technology Department of Inner Mongolia Autonomous Region (2015-2019).

摘要/Abstract

摘要：

由于自然地表像元在长期观测中容易发生时空失相干,利用时序InSAR（Synthetic Aperture Radar Interferometry）技术对其开展形变监测会面临可用形变测量点不足的挑战。针对这一问题,提出一种改进的小基线（Small Baseline Subset,SBAS）方法。该方法改进了传统SBAS中初始高相干像元筛选及相位滤波过程：首先利用拟合优度检验,并结合相干性阈值条件来识别同质像元;然后根据同质像元数量将所有像元分成2部分,即PS（Persistent Scatterers）候选点和DS（Distributed Scatterers）候选点;其次分别在这两部分像元中开展初始高相干PS点及DS点筛选;最后对选出的高相干PS点及DS点进行加权相位滤波。利用覆盖北京平原区西北部（含城区及山区）的27景ENVISAT ASAR影像开展的形变监测实验表明：与2个参考方法相比,该方法能够有效扩展形变结果上的测量点数量和覆盖范围,测量点数量分别提高了22.6%及27.6%,且自然地表的形变测量点密度得到了明显提升。同时,研究区形变结果与4个连续GPS站的位移数据有很好的一致性,证明了该方法在地表形变反演中的有效性及优越性。

关键词: SBAS, 形变, 时序InSAR, PS-InSAR, PS, DS

Abstract:

Owning to that the pixels in natural terrains are prone to spatial-temporal decorrelation during the long-term observation, using time-series InSAR (Synthetic Aperture Interferometry) technique to carry out deformation monitoring of natural terrains will face the challenge of lacking of available deformation measurement points. To solve this problem, an improved Small Baseline Subset (SBAS) method is proposed. It improves the selection process of initial high coherent pixels and phase filtering in conventional SBAS. Firstly, it uses the goodness of fit and the coherence threshold condition to identify statistically homogeneous pixels (SHP). After this, all pixels are divided into two parts base on the number of SHP, i.e. Persistent Scatterers (PS) candidates and Distributed Scatterers (DS) candidates. Then, initial high coherent PS and DS are selected from these two parts respectively. Finally those selected high coherent PS and DS are filtered by a weighted phase filter. The deformation monitoring experiment with 27 ENVISAT ASAR images, acquired over the northwest part of Beijing plain shows that: compared with StaMPS-PS (refers to the PS-InSAR in StaMPS) method and StaMPS-SBAS (refers to the SBAS in StaMPS) method, the improved method can effectively extend the quantity and coverage of deformation measurement points. The quantity of measurement points is increased by 22.6% and 27.6% respectively, and the deformation result of natural terrains is improved effectively. The deformation result of this study area is in good agreement with the displacement of 4 continuous GPS stations. Experimental results prove the effectiveness and superiority of this method in the inversion of ground deformation.

Key words: SBAS, deformation, time-series InSAR, PS-InSAR, PS, DS

黄俊松, 曾琪明, 高胜, 焦健, 胡乐银. 适用于自然地表形变反演的小基线集方法[J]. 地球信息科学学报, 2018, 20(4): 440-451.DOI:10.12082/dqxxkx.2018.170579

HUANG Junsong,ZENG Qiming,GAO Sheng,JIAO Jian,HU Leyin. An Improved Small Baseline Subset Method for Deformation Retrieval of Natural Terrains[J]. Journal of Geo-information Science, 2018, 20(4): 440-451.DOI:10.12082/dqxxkx.2018.170579

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参考文献 28

[1]	Hanssen R F. Radar interferometry data interpretation and error analysis[J]. Journal of the Graduate School of the Chinese Academy of Sciences, 2001,2(1):V5-577-V5-580. doi: 10.1007/0-306-47633-9
[2]	Hooper A, Zebker H, Segall P, et al.A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers[J]. Geophysical Research Letters, 2004,31(23):1-5. doi: 10.1029/2004GL021737
[3]	高胜,曾琪明,焦健,等.永久散射体雷达干涉研究综述[J].遥感技术与应用,2016,31(1):86-94. doi: 10.11873/j.issn.1004-0323.2016.1.0086
	[ Gao S, Zeng Q, Jiao J, et al.A review on persistent scatterer interferometric synthetic aperture radar[J]. Remote Sensing Technology & Application, 2016,31(1):86-94. ] doi: 10.11873/j.issn.1004-0323.2016.1.0086
[4]	Berardino P, Fornaro G, Lanari R, et al.A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002,40(11):2375-2383.
[5]	Schmidt D, Bürgmann R. Time-dependent land uplift and subsidence in the Santa Clara Valley, California[J]. Journal of Geophysical Research Solid Earth, 2003,108(B9): ETG 4-1. doi: 10.1029/2002JB002267
[6]	Gong W, Thiele A, Hinz S, et al.Comparison of small baseline interferometric SAR processors for estimating ground deformation[J]. Remote Sensing, 2016,8(4):330. doi: 10.3390/rs8040330
[7]	Hooper A.A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches[J]. Geophysical Research Letters, 2008,35(16):96-106. doi: 10.1029/2008GL034654
[8]	Lanari R, Mora O, Manunta M, et al.A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms[J]. Geoscience & Remote Sensing IEEE Transactions on, 2004,42(7):1377-1386. doi: 10.1109/TGRS.2004.828196
[9]	范锐彦,焦健,高胜,等. InSAR时序分析高相干目标选取方法比较研究[J].地球信息科学学报,2016,18(6):805-814. doi: 10.3724/SP.J.1047.2016.00805
	[ Fan R Y, Jiao J, Gao S, et al.2016. Comparison research of high coherent target selection based on InSAR time series analysis[J]. Journal of Geo-information Science, 2016,18(6):805-814. ] doi: 10.3724/SP.J.1047.2016.00805
[10]	Mora O, Mallorqui J J, Broquetas A.Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images[J]. IEEE Transactions on Geoscience & Remote Sensing, 2003,41(10):2243-2253.
[11]	陈强,刘国祥,李永树,等.干涉雷达永久散射体自动探测--算法与实验结果[J].测绘学报,2006,35(2):112-117.
	[Chen Q, Liu G X, Li Y S, et al.Automated detection of permanent scatterers in radar interferometry: Algorithm and testing results[J]. Acta Geodaetica Et Cartographica Sinica, 2015,35(2):112-117. ]
[12]	曲世勃,王彦平,洪文. PSDInSAR的永久散射体时序选择方法[J].电子与信息学报,2011,33(2):381-387. doi: 10.3724/SP.J.1146.2010.00185
	[ Qu S B, Wang Y P, Hong W.The PS selection method using temporal information in PSDInSAR technique[J]. Dianzi Yu Xinxi Xuebao/Journal of Electronics & Information Technology, 2011,33(2):381-387. ] doi: 10.3724/SP.J.1146.2010.00185
[13]	Hooper A, Spaans K, Bekaert D, et al.StaMPS/MTI manual[J]. Delft institute of earth observation and space systems delft university of technology, Kluyverweg, 2010,1:2629.
[14]	Wegmǜller U.GAMMA IPTA Processing Example Luxemburg, GAMMA Technical Report[R]. Luxeburg: GAMMA, 2005.
[15]	丁伟. PSInSAR点目标提取及相位解缠技术研究[D].长沙:中南大学,2011.
	[ Ding W.Study of PSInSAR on the technique of points selection and phase unwrapping[D]. Changsha: Central South University, 2011. ]
[16]	Ferretti A, Fumagalli A, Novali F, et al.A new algorithm for processing interferometric data-stacks: SqueeSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011,49(9):3460-3470.
	[17 Kvam P H, Vidakovic B. Nonparametric statistics with applications to science and engineering[M]. Hoboken, New Jersey: John Wiley & Sons, Inc., 2007.
[18]	Ferretti A, Prati C, Rocca F.Permanent scatterers in SAR interferometry[J]. IEEE Transactions on geoscience and remote sensing, 2001,39(1):8-20. doi: 10.1109/36.898661
[19]	罗小军,黄丁发,刘国祥.时序差分雷达干涉中永久散射体的自动探测[J].西南交通大学学报,2007,42(4):414-418. doi: 10.3969/j.issn.0258-2724.2007.04.006
	[ Luo X, Huang D, Liu G.Automated detection of permanent scatterers in time serial differential radar interferometry[J]. Journal of Southwest Jiaotong University, 2007,42(4):414-418. ] doi: 10.3969/j.issn.0258-2724.2007.04.006
[20]	Xia Y.Homogeneous pixel selection for distributed scatterers using multitemporal SAR data stacks[D]. Technical University of Munich (TUM), 2016.
[21]	何庆成,刘文波,李志明.华北平原地面沉降调查与监测[J].高校地质学报,2006,12(2):195-209. doi: 10.3969/j.issn.1006-7493.2006.02.006
	[ Qing-Cheng H E, Liu W B, Zhi-Ming L I. Land subsidence survey and monitoring in the North China Plain[J]. Geological Journal of China Universities, 2006,12(2):195-209. ] doi: 10.3969/j.issn.1006-7493.2006.02.006
[22]	Ng A H-M, Ge L, Li X, et al. Monitoring ground deformation in Beijing, China with persistent scatterer SAR interferometry[J]. Journal of Geodesy, 2012,86(6):375-392. doi: 10.1007/s00190-011-0525-4
[23]	Zebker H A, Villasenor J.Decorrelation in interferometric radar echoes[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992,30(5):950-959. doi: 10.1109/36.175330
[24]	Doin M P, Lodge F, Guillaso S, et al.Presentation of the small baseline NSBAS processing chain on a case example: The ETNA deformation monitoring from 2003 to 2010 using ENVISAT data[C]. FRINGE 2011 ESA Conference, 2011:954-966.
[25]	Agram P S.Persistent scatterer interferometry in natural terrain[D]. Stanford: Stanford university, 2010.
[26]	Zhang Y-Q, Wang S, Wang R.Research on monitoring land subsidence in Beijing plain area using PS-InSAR technology[J]. Spectroscopy and Spectral Analysis, 2014,34(7):1898-1902. doi: 10.3964/j.issn.1000-0593(2014)07-1898-05 pmid: 25269304
[27]	Luo Y.Primary investigation of formation and genetic mechanism of land subsidence based on PS-InSAR technology in Beijing[J]. Spectroscopy and Spectral Analysis, 2014,34(8):2185-2189. doi: 10.3964/j.issn.1000-0593(2014)08-2185-05 pmid: 25474959
[28]	Wang Y, Liu Y, Hu L.GPS-based research on changes of land subsidence in Beijing from 2007 to 2012[J]. Journal of Catastrophology, 2015,30:11-15.

序号	获取时间	垂直基线 B⊥/m	序号	获取时间	垂直基线B⊥/m
1	2007-01-22	586	15	2009-05-11	-165
2	2007-02-26	-46	16	2009-08-24	0
3	2007-05-07	-195	17	2009-09-28	556
4	2007-06-11	-237	18	2009-11-02	-205
5	2007-07-16	-107	19	2010-01-11	-252
6	2007-12-03	263	20	2010-02-15	423
7	2008-01-07	-524	21	2010-05-22	61
8	2008-02-11	274	22	2010-04-26	216
9	2008-03-17	-235	23	2010-05-31	-96
10	2008-01-30	-67	24	2010-07-05	85
11	2008-08-04	15	25	2010-08-09	-370
12	2008-09-08	239	26	2010-09-13	80
13	2008-10-13	-200	27	2010-10-18	329
14	2009-04-06	462	-	-	-

α	平均值	最大值
0.05	151.58	225
0.15	132.26	225
0.25	107.00	225
0.45	74.31	224
0.65	36.00	208
0.70	7.52	166
0.80	7.52	166
0.90	7.52	166

	初始选取的高相干像元数量及对应相干性	形变测量点数量及对应相干性	淘汰率/%	形变测量点密度/(个/km²)
StaMPS-PS	354 116/0.31	93 646/0.39	73.6	32.31
StaMPS-SBAS	835 977/0.30	89 967/0.32	89.1	31.04
本方法	824 972/0.36	114 765/0.30	86.1	39.60

	形变测量点总数	时序平均相干性低于0.11的像元			时序平均相干性低于0.20的像元				时序平均相干性低于0.30的像元
	形变测量点总数	占比/%		平均相干性	密度/ (个/km²)	占比/%	平均相干性	密度/ (个/km²)	占比/%	平均相干性	密度/ (个/km²)
StaMPS-PS	93 646	0.29	0.10	0.24	5.00	0.16	2.23		24.95	0.24	9.18
StaMPS-SBAS	89 967	2.95	0.10	2.29	18.03	0.15	7.73		45.02	0.21	16.58
本方法	114 765	3.64	0.10	3.61	27.10	0.15	14.81		53.71	0.20	25.22

	BJSH			CHAO		CHPN			DSQI
	位移差平均值		位移差标准差		位移差平均值	位移差标准差	位移差平均值	位移差标准差	位移差平均值	位移差标准差
StaMPS-PS	2.43	3.34		8.36	6.69	2.38	1.42		15.76	10.33
StaMPS-SBAS	3.57	4.42		5.32	5.36	2.27	1.31		8.70	6.81
本方法	3.31	4.05		5.67	5.52	2.75	1.51		13.31	9.21

适用于自然地表形变反演的小基线集方法

An Improved Small Baseline Subset Method for Deformation Retrieval of Natural Terrains

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