Multi-view clustering-based time series empirical tropospheric delay correction

Tropospheric delays (TDs) still hinder the millimeter-scale measurement accuracy of interferometric synthetic aperture radar (InSAR). Toward higher accuracy, this letter presents a new time series TDs correction method. The rationale behind the proposed method is that multi-view clustering (MvC) is...

Full description

Saved in:

Bibliographic Details
Main Authors:	Gao, Zhuang, He, Xiufeng, Ma, Zhangfeng, Shi, Guoqiang, Sha, Pengcheng
Other Authors:	Asian School of the Environment
Format:	Article
Language:	English
Published:	2023
Subjects:	Science::Geology Delays Time Series Analysis
Online Access:	https://hdl.handle.net/10356/170682
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-170682
record_format	dspace
spelling	sg-ntu-dr.10356-1706822023-09-26T01:24:50Z Multi-view clustering-based time series empirical tropospheric delay correction Gao, Zhuang He, Xiufeng Ma, Zhangfeng Shi, Guoqiang Sha, Pengcheng Asian School of the Environment Earth Observatory of Singapore Science::Geology Delays Time Series Analysis Tropospheric delays (TDs) still hinder the millimeter-scale measurement accuracy of interferometric synthetic aperture radar (InSAR). Toward higher accuracy, this letter presents a new time series TDs correction method. The rationale behind the proposed method is that multi-view clustering (MvC) is introduced to identify the spatiotemporal TDs behaviors, particularly, in which the one-pass multi-view clustering (OPMC) algorithm is employed to perform window segmentation rather than sticking to the commonly used boxcar windows. Next, a phase-elevation network correction model in each cluster is constructed by fully considering the spatiotemporal phase information. Besides, an iterative weighted scheme is designed to further enhance the robustness of the estimated model parameters. The Sentinel-1 datasets covering the southwest mountainous area, China, confirm the effectiveness of the new method. This work was supported in part by the National Natural Science Foundation of China under Grant 41830110 and in part by the Fundamental Research Funds for the Central Universities under Grant B230205015. 2023-09-26T01:24:50Z 2023-09-26T01:24:50Z 2023 Journal Article Gao, Z., He, X., Ma, Z., Shi, G. & Sha, P. (2023). Multi-view clustering-based time series empirical tropospheric delay correction. IEEE Geoscience and Remote Sensing Letters, 20, 4005705-. https://dx.doi.org/10.1109/LGRS.2023.3273854 1545-598X https://hdl.handle.net/10356/170682 10.1109/LGRS.2023.3273854 2-s2.0-85159835249 20 4005705 en IEEE Geoscience and Remote Sensing Letters © 2023 IEEE. All rights reserved.
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Science::Geology Delays Time Series Analysis
spellingShingle	Science::Geology Delays Time Series Analysis Gao, Zhuang He, Xiufeng Ma, Zhangfeng Shi, Guoqiang Sha, Pengcheng Multi-view clustering-based time series empirical tropospheric delay correction
description	Tropospheric delays (TDs) still hinder the millimeter-scale measurement accuracy of interferometric synthetic aperture radar (InSAR). Toward higher accuracy, this letter presents a new time series TDs correction method. The rationale behind the proposed method is that multi-view clustering (MvC) is introduced to identify the spatiotemporal TDs behaviors, particularly, in which the one-pass multi-view clustering (OPMC) algorithm is employed to perform window segmentation rather than sticking to the commonly used boxcar windows. Next, a phase-elevation network correction model in each cluster is constructed by fully considering the spatiotemporal phase information. Besides, an iterative weighted scheme is designed to further enhance the robustness of the estimated model parameters. The Sentinel-1 datasets covering the southwest mountainous area, China, confirm the effectiveness of the new method.
author2	Asian School of the Environment
author_facet	Asian School of the Environment Gao, Zhuang He, Xiufeng Ma, Zhangfeng Shi, Guoqiang Sha, Pengcheng
format	Article
author	Gao, Zhuang He, Xiufeng Ma, Zhangfeng Shi, Guoqiang Sha, Pengcheng
author_sort	Gao, Zhuang
title	Multi-view clustering-based time series empirical tropospheric delay correction
title_short	Multi-view clustering-based time series empirical tropospheric delay correction
title_full	Multi-view clustering-based time series empirical tropospheric delay correction
title_fullStr	Multi-view clustering-based time series empirical tropospheric delay correction
title_full_unstemmed	Multi-view clustering-based time series empirical tropospheric delay correction
title_sort	multi-view clustering-based time series empirical tropospheric delay correction
publishDate	2023
url	https://hdl.handle.net/10356/170682
_version_	1779156403791855616

Multi-view clustering-based time series empirical tropospheric delay correction

Similar Items