A Novel Two-Stage Learning-Based Phase Unwrapping Algorithm via Multimodel Fusion

A Novel Two-Stage Learning-Based Phase Unwrapping Algorithm via Multimodel Fusion

Phase unwrapping (PhU) is one of the key steps in interferometric synthetic aperture radar (InSAR) data processing, and it is a considerable challenge for PhU in regions with high-noise and large-gradient changes. Deep learning phase unwrapping (DLPU) can better solve this problem. However, a single...

Full description

Saved in:
Bibliographic Details
Main Authors: Chao Yan, Tao Li, Yandong Gao, Shijin Li, Xiang Zhang, Xuefei Zhang, Di Zhang, Huiqin Liu
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Deep learning (DL)
interferometric synthetic aperture radar (INSAR)
<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$L_{1}$</tex-math> </inline-formula> </named-content>-norm
phase unwrapping (PhU)
Online Access:https://ieeexplore.ieee.org/document/10884057/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/10884057/

Similar Items