Phase estimation of distributed scatterer based on singular value decomposition

doi:10.11947/j.AGCS.2024.20230017

Abstract

Abstract:

The covariance matrix is the basis for estimating the phase of distributed scatterer (DS) when using conventional algorithm. Therefore, a full combination of SAR data should be generated firstly to construct the sample covariance matrix (SCM). However, this process is not only computationally expensive but also consumes a large amount of storage space. In this paper, a fast algorithm, referred to as SVDI (SVD to interferometric phase matrix), for estimating the phase of DS based on singular value decomposition is proposed. SVDI estimates the phase of DS from the interferometric phase matrix constructed by single-master interferograms rather than the SCM constructed by multi-master interferograms (i.e., the full combination of SAR data). Therefore, SVDI can effectively improve the computationally efficient and save the storage space. Moreover, it is theoretically proved that the results of SVDI are consistent with the conventional eigenvalue decomposition (EVD) method based on an assumption. The simulated and real SAR data is used to verify the feasibility and reliability of SVDI. The experimental results show that the phase and deformation estimation accuracy of SVDI is consistent with that of the conventional method.

Key words: distributed scatterer, phase estimation, sample covariance matrix, eigen value decomposition, singular value decomposition

CLC Number:

P258

Chuanguang ZHU, Jixian ZHANG, Sichun LONG, Ronghua YANG, Wenhao WU, Liya ZHANG. Phase estimation of distributed scatterer based on singular value decomposition[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(7): 1308-1320.

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组	γ₀	γ_∞	τ/d
1	0.60	0.00	50
2	0.60	0.20	50
3	0.60	0.05	180

项目	参数
试验区	章丘	休斯敦
数据集	Sentinel-1A	Sentinel-1A
Path	142	143
Subswath	IW3	IW2
日期	2017-07—2022-11	2017-01—2020-12
数据量	157	119
轨道模式	Ascending	Descending
入射角/（°）	43.8	39.2
航向角/（°）	-13.2	-167.3

点名	最大互差/mm	互差绝对值小于2 mm的比例/（%）	标准差/mm
Z1	3.2	99.987	0.5
Z2	2.0	100	0.3
Z3	0.2	100	0.1
Z4	0.3	100	0.1
Z5	2.5	99.994	0.5
H1	0.8	100	0.1
H2	0.1	100	0.0
H3	0.1	100	0.0
H4	0.1	100	0.0
H5	0.9	100	0.2