A Deep Convolutional Neural Network With Multiscale Feature Dynamic Fusion for InSAR Phase Filtering

A Deep Convolutional Neural Network With Multiscale Feature Dynamic Fusion for InSAR Phase Filtering

Interferometric phase filtering is a crucial step in the interferometric synthetic aperture radar (InSAR) data processing, which is also important for improving the accuracy of topography mapping and deformation monitoring. Most of the commonly used phase filtering methods perform windowing computat...

Full description

Bibliographic Details
Main Authors: Wang Yang, Yi He, Lifeng Zhang, Sheng Yao, Zhiqing Wen, Shengpeng Cao, Zhanao Zhao, Yi Chen, Yali Zhang
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Deep convolutional neural network (DCNN)
feature learning
interferometric phase filtering
interferometric synthetic aperture radar (InSAR)
Online Access:https://ieeexplore.ieee.org/document/9858594/

Internet

https://ieeexplore.ieee.org/document/9858594/

Similar Items