{"title":"CBIPDNet: A Novel Method for InSAR Deformation Interferometric Phase Filtering Using Deep Learning Network","authors":"Yandong Gao;Jiaqi Yao;Wei Zhou;Nanshan Zheng;Shijin Li;Yu Tian","doi":"10.1109/JSTARS.2024.3453071","DOIUrl":null,"url":null,"abstract":"The denoising of phase is a crucial process that impacts the accuracy of data processing in differential interferometric synthetic aperture radar. Especially in the area of large-gradient deformation, the phase filtering method is very easy to cause phase losses. This has a significant impact on the final deformation acquisition. To address this issue, here, a deep convolutional blind denoising network-based interferometric phase filtering method, named CBIPDNet, is proposed. Different from the previously proposed deep learning phase filtering methods, CBIPDNet does not add noise to the input before filtering, but adds noise to the input during the training process. Furthermore, CBIPDNet uses a CNN structure for adaptive noise estimation and uses a residual module for nonblind filtering. Therefore, CBIPDNet can be considered as an adaptive phase filtering algorithm. More importantly, the added noise is composed of heteroscedastic Gaussian noise + simulated real noise of the imaging process, which is closer to the real interferometric noise phase. Moreover, the denoising effect of targets of different scales through the asymmetric loss function has been significantly improved, which can improve the detail preservation ability of regions with substantial gradient deformations. The experimental results demonstrate that CBIPDNet is capable of enhancing phase quality and increasing phase unwrapping accuracy compared to the current interferometric filtering methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670027","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10670027/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The denoising of phase is a crucial process that impacts the accuracy of data processing in differential interferometric synthetic aperture radar. Especially in the area of large-gradient deformation, the phase filtering method is very easy to cause phase losses. This has a significant impact on the final deformation acquisition. To address this issue, here, a deep convolutional blind denoising network-based interferometric phase filtering method, named CBIPDNet, is proposed. Different from the previously proposed deep learning phase filtering methods, CBIPDNet does not add noise to the input before filtering, but adds noise to the input during the training process. Furthermore, CBIPDNet uses a CNN structure for adaptive noise estimation and uses a residual module for nonblind filtering. Therefore, CBIPDNet can be considered as an adaptive phase filtering algorithm. More importantly, the added noise is composed of heteroscedastic Gaussian noise + simulated real noise of the imaging process, which is closer to the real interferometric noise phase. Moreover, the denoising effect of targets of different scales through the asymmetric loss function has been significantly improved, which can improve the detail preservation ability of regions with substantial gradient deformations. The experimental results demonstrate that CBIPDNet is capable of enhancing phase quality and increasing phase unwrapping accuracy compared to the current interferometric filtering methods.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.