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An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN

Interferogram filtering is an essential step in processing data from interferometric synthetic aperture radar (InSAR), which greatly improves the accuracy of terrain reconstruction and deformation monitoring. Most traditional interferogram filtering methods achieve noise suppression and detail prese...

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Autores principales: Yang, Wang, He, Yi, Yao, Sheng, Zhang, Lifeng, Cao, Shengpeng, Wen, Zhiqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9416368/
https://www.ncbi.nlm.nih.gov/pubmed/36015725
http://dx.doi.org/10.3390/s22165956
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author Yang, Wang
He, Yi
Yao, Sheng
Zhang, Lifeng
Cao, Shengpeng
Wen, Zhiqing
author_facet Yang, Wang
He, Yi
Yao, Sheng
Zhang, Lifeng
Cao, Shengpeng
Wen, Zhiqing
author_sort Yang, Wang
collection PubMed
description Interferogram filtering is an essential step in processing data from interferometric synthetic aperture radar (InSAR), which greatly improves the accuracy of terrain reconstruction and deformation monitoring. Most traditional interferogram filtering methods achieve noise suppression and detail preservation through morphological estimation based on the statistical properties of the interferogram in the spatial or frequency domain. However, as the interferogram’s spatial distribution is diverse and complex, traditional filtering methods struggle to adapt to different distribution and noise conditions and cannot handle detail preservation and noise suppression simultaneously. The study proposes a convolutional neural network (CNN)-based multi-level feature fusion model for interferogram filtering that differs from the traditional feedforward neural network (FNN). Adopting a multi-depth multi-path convolution strategy, the method preserves phase details and suppresses noise during interferogram filtering. In filtering experiments based on simulated data, qualitative and quantitative evaluations were used to validate the performance and generalization capabilities of the proposed method. The method’s applicability was evaluated by visual observation during filtering and unwrapping experiments on real data, and the time-series deformation acquired by time series (TS)-InSAR technique is used to evaluate the effect of interferogram filters on deformation monitoring accuracy. Compared to commonly used interferogram filtering methods, the proposed method has significant advantages in terms of performance and efficiency. The study findings suggest new directions for research on high-precision InSAR data processing and provide technical support for practical applications of InSAR.
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spelling pubmed-94163682022-08-27 An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN Yang, Wang He, Yi Yao, Sheng Zhang, Lifeng Cao, Shengpeng Wen, Zhiqing Sensors (Basel) Article Interferogram filtering is an essential step in processing data from interferometric synthetic aperture radar (InSAR), which greatly improves the accuracy of terrain reconstruction and deformation monitoring. Most traditional interferogram filtering methods achieve noise suppression and detail preservation through morphological estimation based on the statistical properties of the interferogram in the spatial or frequency domain. However, as the interferogram’s spatial distribution is diverse and complex, traditional filtering methods struggle to adapt to different distribution and noise conditions and cannot handle detail preservation and noise suppression simultaneously. The study proposes a convolutional neural network (CNN)-based multi-level feature fusion model for interferogram filtering that differs from the traditional feedforward neural network (FNN). Adopting a multi-depth multi-path convolution strategy, the method preserves phase details and suppresses noise during interferogram filtering. In filtering experiments based on simulated data, qualitative and quantitative evaluations were used to validate the performance and generalization capabilities of the proposed method. The method’s applicability was evaluated by visual observation during filtering and unwrapping experiments on real data, and the time-series deformation acquired by time series (TS)-InSAR technique is used to evaluate the effect of interferogram filters on deformation monitoring accuracy. Compared to commonly used interferogram filtering methods, the proposed method has significant advantages in terms of performance and efficiency. The study findings suggest new directions for research on high-precision InSAR data processing and provide technical support for practical applications of InSAR. MDPI 2022-08-09 /pmc/articles/PMC9416368/ /pubmed/36015725 http://dx.doi.org/10.3390/s22165956 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Wang
He, Yi
Yao, Sheng
Zhang, Lifeng
Cao, Shengpeng
Wen, Zhiqing
An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN
title An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN
title_full An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN
title_fullStr An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN
title_full_unstemmed An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN
title_short An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN
title_sort insar interferogram filtering method based on multi-level feature fusion cnn
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9416368/
https://www.ncbi.nlm.nih.gov/pubmed/36015725
http://dx.doi.org/10.3390/s22165956
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