Deep Learning-Based Joint Local and Non-local Phase Filtering for InSAR Images

WANG Jie; LIU Jiahang; LING Xinpeng; DUAN Zexian

doi:10.13203/j.whugis20240052

您当前的位置：

首页 >

文章列表页 >

Deep Learning-Based Joint Local and Non-local Phase Filtering for InSAR Images

Photogrammetry and Remote Sensing | 更新时间：2026-11-05

- Deep Learning-Based Joint Local and Non-local Phase Filtering for InSAR Images
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 2, Pages: 367-379(2026)
- 作者机构：
  
  1.南京航空航天大学航天学院，江苏南京，210016
  2.空间光电探测与感知工业和信息化部重点实验室，江苏南京，210016
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20240052
  CLC： P237
- Received：25 April 2024，
  
  Published：05 February 2026
- 稿件说明：
移动端阅览
王杰,柳稼航,凌信鹏，等.基于深度学习的InSAR影像局部与非局部联合相位滤波[J].武汉大学学报（信息科学版）,2026,51(2):367-379.

WANG Jie,LIU Jiahang,LING Xinpeng,et al.Deep Learning-Based Joint Local and Non-local Phase Filtering for InSAR Images[J].Geomatics and Information Science of Wuhan University,2026,51(2):367-379.
王杰,柳稼航,凌信鹏，等.基于深度学习的InSAR影像局部与非局部联合相位滤波[J].武汉大学学报（信息科学版）,2026,51(2):367-379. DOI：10.13203/j.whugis20240052

WANG Jie,LIU Jiahang,LING Xinpeng,et al.Deep Learning-Based Joint Local and Non-local Phase Filtering for InSAR Images[J].Geomatics and Information Science of Wuhan University,2026,51(2):367-379. DOI：10.13203/j.whugis20240052

摘要

相位滤波是合成孔径雷达干涉测量（interferometric synthetic aperture radar

InSAR）数据处理的关键技术之一，干涉图质量显著影响后续处理的精度。传统方法滤波效果差且效率低下，深度学习方法潜力大但目前精度受限。为有效提升滤波效果，结合卷积神经网络（convolutional neural network

CNN）与Transformer网络的优势，提出了一种局部与非局部联合的InSAR影像相位滤波方法。该方法一方面利用Transformer全局特征提取能力强的特点，构建了CNN与Transformer相结合的相位滤波网络（phase filtering network combining CNN and Transformer

PFCT），能够同步进行局部与非局部滤波，从而克服现有方法因忽略干涉条纹非局部特征而导致的精度限制；另一方面，设计了一种新的复合损失函数，让网络迭代优化过程中同时保持去噪与条纹结构两方面的性能。在仿真实验中，PFCT的均方误差比次优算法降低了15.5%，结构相似性指标提高了5.3%；在实测实验中，残点去除性能比次优算法提高了1.8%。结果表明，PFCT的滤波性能优于目前的主要算法，能获得更好的效果。

Abstract

Objectives

Interferometric synthetic aperture radar (InSAR) acquires ground elevation information and surface deformation information by interferometrically processing multiple synthetic aperture radar (SAR) complex images with certain temporal and spatial correlations. It has been widely applied in high-precision 3D mapping

as well as in the monitoring and early warning of geological disasters such as earthquake deformation

volcanic movement

and surface subsidence. Phase filtering is one of the key technologies in InSAR data processing

and the quality of interferograms significantly affects the accuracy of subsequent processing steps such as phase unwrapping and digital elevation model generation

but filtering effectiveness is often compromised by complex noise in the interferometric phase. The traditional phase filtering methods with manual parameter adjustment have not only low filtering accuracy but also low computational efficiency. Deep learning-based phase filtering methods have demonstrated great potential in automating this process and improving performance

but current convolutional neural networks (CNN) based methods overlook the non-local features causing limiting accuracy. Transformer-based networks

on the other hand

have strong global modeling capabilities and can extract non-local features from interference fringes

but they frequently struggle with capturing fine-grained local details and spatial structures of interference fringes

which are equally crucial for precise phase restoration.

Methods

Deep learning is data-driven

yet there is currently no publicly available InSAR interferometric phase dataset. The proposed method generates a clean phase map using shuttle radar topography mission 30 m digital elevation model

and adds simulated noise to produce an interferometric phase map

thereby creating a dataset for training the network. Besides

to effectively improve the filtering effect

we propose a joint local and non-local phase filtering network combing CNN and Transformer (PFCT) for InSAR images. First

based on the strong global feature extraction capability of the robust Transformer network

the PFCT network structure is proposed for phase filtering. The network performs both local and nonlocal filtering at the same time

avoiding the accuracy limitation problem caused by the neglect of non-local features of interference fringes in existing CNN-based methods while also addressing the local detail deficiency typical of Transformer approaches. The architecture integrates CNN layers for capturing high-frequency details and spatial patterns with Transformer modules for modeling long-range dependencies across the fringe patterns. Then

a new complex loss function which combines point phase loss and phase gradient loss is proposed to guide the network to maintain the integrity of interference fringes while improving filtering performance.

Results

In the simulation data experiment

the proposed method achieves a mean square error index that is 15.5% lower than the suboptimal algorithm

and the structural similarity (SSIM) index is 5.3% higher

indicating superior noise removal and structural preservation ability. These improvements are consistently observed across varying noise levels

confirming the model's robustness. The enhanced SSIM particularly underscores its effectiveness in maintaining critical fringe structures essential for accurate unwrapping. In the true data experiment

the residue removal index is 1.8% higher than the suboptimal method

demonstrating enhanced reliability in practical applications. Visual assessment of filtered interferograms also shows a marked reduction in speckle noise and better preservation of deformation fringes in areas with high phase gradients. The experiments show that the PFCT network model only slightly reduces computational efficiency

but achieves better filtering results than other methods

effectively maintaining the stripe structure while maintaining the filtering effect.

Conclusions

The proposed method has a great filtering effect and powerful generalization ability on InSAR phase filtering across different terrains and noise conditions. By successfully hybridizing CNN and Transformer strengths

it provides a robust and efficient option for high-quality interferogram generation in modern InSAR processing workflows. Future work may focus on further optimizing the network for edge-device deployment and exploring its adaptation to multi-temporal InSAR time series analysis for dynamic monitoring applications.

关键词

Keywords

references

邓云凯，禹卫东，张衡，等 . 未来星载SAR技术发展趋势［J］. 雷达学报， 2020 ， 9 （ 1 ）： 1 - 33 .

DENG Yunkai ， YU Weidong ， ZHANG Heng ， et al . Forthcoming Spaceborne SAR Development ［J］. Journal of Radars ， 2020 ， 9 （ 1 ）： 1 - 33 .

郭华东，张露 . 雷达遥感六十年：四个阶段的发展［J］. 遥感学报， 2019 ， 23 （ 6 ）： 1023 - 1035 .

GUO Huadong ， ZHANG Lu . 60 Years of Radar Remote Sensing： Four‑Stage Development ［J］. Journal of Remote Sensing ， 2019 ， 23 （ 6 ）： 1023 - 1035 .

BAMLER R ， HARTL P . Synthetic Aperture Radar Interferometry ［J］. Inverse Problems ， 1998 ， 14 （ 4 ）： R1 - R54 .

袁煜伟，李志伟，母岷政 . 中国第一代全球大气再分析数据在InSAR大气改正中的应用研究：以南加州地区为例［J］. 武汉大学学报（信息科学版）， 2025 ， 50 （ 5 ）： 959 - 972 .

YUAN Yuwei ， LI Zhiwei ， MU Minzheng . Application of China's First Generation Global Atmospheric Reanalysis Data in InSAR Atmospheric Correction： A Case Study of Southern California ［J］. Geomatics and Information Science of Wuhan University ， 2025 ， 50 （ 5 ）： 959 - 972 .

许文斌，罗兴军，朱建军，等 . InSAR火山形变监测与参数反演研究进展［J］. 武汉大学学报（信息科学版）， 2023 ， 48 （ 10 ）： 1632 - 1642 .

XU Wenbin ， LUO Xingjun ， ZHU Jianjun ， et al . Review of Volcano Deformation Monitoring and Modeling with InSAR ［J］. Geomatics and Information Science of Wuhan University ， 2023 ， 48 （ 10 ）： 1632 - 1642 .

赵峰，张雷昕，王腾，等 . 城市地表形变的双极化Sentinel‑1数据极化时序InSAR技术监测［J］. 武汉大学学报（信息科学版）， 2022 ， 47 （ 9 ）： 1507 - 1514 .

ZHAO Feng ， ZHANG Leixin ， WANG Teng ， et al . Polarimetric Persistent Scatterer Interferometry for Urban Ground Deformation Monitoring with Sentinel‑1 Dual Polarimetric Data ［J］. Geomatics and Information Science of Wuhan University ， 2022 ， 47 （ 9 ）： 1507 - 1514 .

韩炳权，刘振江，陈博，等 . 2022年泸定Mw 6.6地震InSAR同震形变与滑动分布［J］. 武汉大学学报（信息科学版）， 2023 ， 48 （ 1 ）： 36 - 46 .

HAN Bingquan ， LIU Zhenjiang ， CHEN Bo ， et al . Coseismic Deformation and Slip Distribution of the 2022 Luding Mw 6.6 Earthquake Revealed by InSAR Observations ［J］. Geomatics and Information Science of Wuhan University ， 2023 ， 48 （ 1 ）： 36 - 46 .

ZEBKER H A ， VILLASENOR J . Decorrelation in Interferometric Radar Echoes ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 1992 ， 30 （ 5 ）： 950 - 959 .

XU G ， XING M D ， ZHANG L ， et al . Bayesian Inverse Synthetic Aperture Radar Imaging ［J］. IEEE Geoscience and Remote Sensing Letters ， 2011 ， 8 （ 6 ）： 1150 - 1154 .

LANARI R ， FORNARO G ， RICCIO D ， et al . Generation of Digital Elevation Models by Using SIR‑C/X‑SAR Multifrequency Two‑Pass Interferometry： The Etna Case Study ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 1996 ， 34 （ 5 ）： 1097 - 1114 .

LEE J S ， PAPATHANASSIOU K P ， AINSWORTH T L ， et al . A New Technique for Noise Filtering of SAR Interferometric Phase Images ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 1998 ， 36 （ 5 ）： 1456 - 1465 .

BUADES A ， COLL B ， MOREL J M . A Review of Image Denoising Algorithms， with a New One ［J］. Multiscale Modeling & Simulation ， 2005 ， 4 （ 2 ）： 490 - 530 .

DELEDALLE C A ， DENIS L ， TUPIN F . NL‐InSAR： Nonlocal Interferogram Estimation ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 2011 ， 49 （ 4 ）： 1441 - 1452 .

SICA F ， COZZOLINO D ， ZHU X X ， et al . InSAR‐BM3D： A Nonlocal Filter for SAR Interferometric Phase Restoration ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 2018 ， 56 （ 6 ）： 3456 - 3467 .

DABOV K ， FOI A ， KATKOVNIK V ， et al . Image Denoising by Sparse 3‑D Transform‑Domain Collaborative Filtering ［J］. IEEE Transactions on Image Processing ， 2007 ， 16 （ 8 ）： 2080 - 2095 .

XU G ， XING M D ， XIA X G ， et al . Sparse Regularization of Interferometric Phase and Amplitude for InSAR Image Formation Based on Bayesian Representation ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 2015 ， 53 （ 4 ）： 2123 - 2136 .

GOLDSTEIN R M ， WERNER C L . Radar Interferogram Filtering for Geophysical Applications ［J］. Geophysical Research Letters ， 1998 ， 25 （ 21 ）： 4035 - 4038 .

ISOGAWA K ， IDA T ， SHIODERA T ， et al . Deep Shrinkage Convolutional Neural Network for Adaptive Noise Reduction ［J］. IEEE Signal Processing Letters ， 2018 ， 25 （ 2 ）： 224 - 228 .

SICA F ， GOBBI G ， RIZZOLI P ， et al . Φ‑Net： Deep Residual Learning for InSAR Parameters Estimation ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 2021 ， 59 （ 5 ）： 3917 - 3941 .

YU H W ， YANG T X ， ZHOU L F ， et al . PDNet： A Lightweight Deep Convolutional Neural Network for InSAR Phase Denoising ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 2022 ， 60 ： 5239309 .

PU L M ， ZHANG X L ， ZHOU L M ， et al . Nonlocal Feature Selection Encoder‑Decoder Network for Accurate InSAR Phase Filtering ［J］. Remote Sensing ， 2022 ， 14 （ 5 ）： 1174 .

ZHANG K ， ZUO W M ， CHEN Y J ， et al . Beyond a Gaussian Denoiser： Residual Learning of Deep CNN for Image Denoising ［J］. IEEE Transactions on Image Processing ， 2017 ， 26 （ 7 ）： 3142 - 3155 .

LI S ， XU H P ， GAO S ， et al . An Interferometric Phase Noise Reduction Method Based on Modified Denoising Convolutional Neural Network ［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ， 2020 ， 13 ： 4947 - 4959 .

YANG W ， HE Y ， ZHANG L F ， et al . A Deep Convolutional Neural Network with Multiscale Feature Dynamic Fusion for InSAR Phase Filtering ［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ， 2022 ， 15 ： 6687 - 6710 .

VITALE S ， FERRAIOLI G ， PASCAZIO V ， et al . InSAR‑MONet： Interferometric SAR Phase Denoising Using a Multiobjective Neural Network ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 2022 ， 60 ： 5239814 .

PU L M ， ZHANG X L ， ZHOU Z N ， et al . A Phase Filtering Method with Scale Recurrent Networks for InSAR ［J］. Remote Sensing ， 2020 ， 12 （ 20 ）： 3453 .

VASWANI A ， SHAZEER N ， PARMAR N ， et al . Attention Is All You Need ［J］. Advances in Neural Information Processing Systems ， 2017 ， 30 ： 5998 - 6008 .

ZHENG S X ， ZHANG X L ， PU L M ， et al . An InSAR Phase Filtering Method Based on Transformer Network ［C］// IEEE International Geoscience and Remote Sensing Symposium ， Kuala Lumpur， Malaysia ， 2022 .

FERRAIOLI G ， DELEDALLE C A ， DENIS L ， et al . PARISAR： Patch‑Based Estimation and Regularized Inversion for Multibaseline SAR Interferometry ［J］. IEEE Transactions on Geoscience and Remote Sensing ， 2018 ， 56 （ 3 ）： 1626 - 1636 .

MURDACA G ， RUCCI A ， PRATI C . Deep Learning for InSAR Phase Filtering： An Optimized Framework for Phase Unwrapping ［J］. Remote Sensing ， 2022 ， 14 （ 19 ）： 4956 .

RONNEBERGER O ， FISCHER P ， BROX T . U‑Net： Convolutional Networks for Biomedical Image Segmentation ［C］// Medical Image Computing and Computer‑Assisted Intervention ， Munich， Germany ， 2015 .

LIU Z ， LIN Y T ， CAO Y ， et al . Swin Transformer： Hierarchical Vision Transformer Using Shifted Windows ［C］// IEEE/CVF Conference on Computer Vision and Pattern Recognition ， Online ， 2021 .

ZHU X ， MILANFAR P . Automatic Parameter Selection for Denoising Algorithms Using a No‑Reference Measure of Image Content ［J］. IEEE Transactions on Image Processing ， 2010 ， 19 （ 12 ）： 3116 - 3132 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Landslide Susceptibility Assessment Model Based on Positive Sample Buffering with Transformer-CNN Integration：A Case Study of Baoji City

DCMR-Net： A Multi-scale Residual Denoising Network for Seismic Records with Complex Noise

Advances in Multimodal Remote Sensing Image Matching： A Survey from Conventional Approaches to Deep Learning

Reservoir Bank Slope Displacement Prediction Model Based on Lagged Attenuation Effect and Key Factor Selection Strategy

Rooftop Photovoltaic Potential Estimation Approach Based on Orientation Perception and Quantitative Integration

Related Author

QU Wei

BIAN Zice

LI Jiuyuan

TANG Xingyou

CHEN Peinan

CHEN Guanzhi

ZHANG Chengfeng

ZHANG Han

Related Institution

College of Geology Engineering and Geomatics, Chang'an University

State Key Laboratory of Precision Geodesy, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences

School of Earth and Space Sciences, University of Science and Technology of China

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University

Hubei Luojia Laboratory

AI问答

⁰