A Sparse-Model-Driven Network for Efficient and High-Accuracy InSAR Phase Filtering

A Sparse-Model-Driven Network for Efficient and High-Accuracy InSAR Phase Filtering

Phase filtering is a vital step for interferometric synthetic aperture radar (InSAR) terrain elevation measurements. Existing phase filtering methods can be divided into two categories: traditional model-based and deep learning (DL)-based. Previous studies have shown that DL-based methods are freque...

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Bibliographic Details
Main Authors: Nan Wang, Xiaoling Zhang, Tianwen Zhang, Liming Pu, Xu Zhan, Xiaowo Xu, Yunqiao Hu, Jun Shi, Shunjun Wei
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Remote Sensing
Subjects:
interferometric phase filtering
sparse regularization (SR)
deep learning (DL)
neural convolutional network (CNN)
Online Access:https://www.mdpi.com/2072-4292/14/11/2614

Internet

https://www.mdpi.com/2072-4292/14/11/2614

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