祁连山冻土区地表形变的InSAR时序监测与PCA-K-means自动分类

周 红宇, 刘 智远, 魏 志奇
兰州石化职业技术大学

摘要


多年冻土区形变受季节冻融与多年趋势叠加影响，空间差异大、解译困难。本文以祁连山冻土区为例，基
于Sentinel-1升轨数据开展SBAS-InSAR反演，获取LOS位移序列与平均速率，并结合PCA-K-means对像元时序特
征降维聚类，实现形变模式自动分区。结果显示研究区整体以低速率为主，局部呈带状/斑块状异常；聚类归纳5类
典型模式，可区分季节冻融主导、持续沉降与持续抬升过程，为冻土退化监测与工程风险识别提供自动化方法支撑。

关键词


祁连山；多年冻土；InSAR；时序形变；K-means聚类；自动分类

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参考


[1]程国栋，赵林，李韧，等.青藏高原多年冻土特

征、变化及影响[J].科学通报，2019，64（27）：2783-

2795.

[2]吴吉春，盛煜，于晖，等.祁连山中东部的冻土特征（Ⅰ）：多年冻土分布[J].冰川冻土，2007，29（3）：

418-425.

[3]Hanssen R F. Radar Interferometry: Data

Interpretation and Error Analysis[M]. Dordrecht: Kluwer

Academic Publishers, 2001.

[4]Crosetto M., Monserrat O., Cuevas-González M.,

Devanthéry N., Crippa B. Persistent Scatterer Interferometry:

A review[J]. ISPRS Journal of Photogrammetry and Remote

Sensing, 2016, 115: 78-89. https://doi.org/10.1016/

j.isprsjprs.2015.10.011

[5]Berti M, Corsini A, Franceschini S, Iannacone J P.

Automated classification of Persistent Scatterers Interferometry

time series[J]. Natural Hazards and Earth System Sciences,

2013, 13: 1945-1958.

[6]Mirmazloumi S M, et al. Classification of ground

deformation using Sentinel-1 persistent scatterer interferometry

time series[J]. GIScience & Remote Sensing, 2022.

[7]MacQueen J. Some methods for classification and

analysis of multivariate observations[C]// Proceedings of

the 5th Berkeley Symposium on Mathematical Statistics and

Probability. 1967: 281-297.

[8]Berardino P, Fornaro G, Lanari R, Sansosti E. 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.

[9]Goldstein R M, Werner C L. Radar interferogram

filtering for geophysical applications[J]. Geophysical Research

Letters, 1998, 25(21): 4035-4038.

[10]Costantini M. A novel phase unwrapping method

based on network programming[J]. IEEE Transactions on

Geoscience and Remote Sensing, 1998, 36(3): 813-821.


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