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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9416368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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