Next Article in Journal
A Quantitative Framework for Analyzing Spatial Dynamics of Flood Events: A Case Study of Super Cyclone Amphan
Next Article in Special Issue
Monitoring the Vertical Land Motion of Tide Gauges and Its Impact on Relative Sea Level Changes in Korean Peninsula Using Sequential SBAS-InSAR Time-Series Analysis
Previous Article in Journal
Assessment of Global Ionospheric Maps Performance by Means of Ionosonde Data
Previous Article in Special Issue
Monitoring Littoral Platform Downwearing Using Differential SAR Interferometry

A Phase Filtering Method with Scale Recurrent Networks for InSAR

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(20), 3453;
Received: 2 September 2020 / Revised: 18 October 2020 / Accepted: 18 October 2020 / Published: 21 October 2020
(This article belongs to the Special Issue InSAR in Remote Sensing)
Phase filtering is a key issue in interferometric synthetic aperture radar (InSAR) applications, such as deformation monitoring and topographic mapping. The accuracy of the deformation and terrain height is highly dependent on the quality of phase filtering. Researchers are committed to continuously improving the accuracy and efficiency of phase filtering. Inspired by the successful application of neural networks in SAR image denoising, in this paper we propose a phase filtering method that is based on deep learning to efficiently filter out the noise in the interferometric phase. In this method, the real and imaginary parts of the interferometric phase are filtered while using a scale recurrent network, which includes three single scale subnetworks based on the encoder-decoder architecture. The network can utilize the global structural phase information contained in the different-scaled feature maps, because RNN units are used to connect the three different-scaled subnetworks and transmit current state information among different subnetworks. The encoder part is used for extracting the phase features, and the decoder part restores detailed information from the encoded feature maps and makes the size of the output image the same as that of the input image. Experiments on simulated and real InSAR data prove that the proposed method is superior to three widely-used phase filtering methods by qualitative and quantitative comparisons. In addition, on the same simulated data set, the overall performance of the proposed method is better than another deep learning-based method (DeepInSAR). The runtime of the proposed method is only about 0.043s for an image with a size of 1024×1024 pixels, which has the significant advantage of computational efficiency in practical applications that require real-time processing. View Full-Text
Keywords: interferometric synthetic aperture radar; scale recurrent network; phase filtering interferometric synthetic aperture radar; scale recurrent network; phase filtering
Show Figures

Graphical abstract

MDPI and ACS Style

Pu, L.; Zhang, X.; Zhou, Z.; Shi, J.; Wei, S.; Zhou, Y. A Phase Filtering Method with Scale Recurrent Networks for InSAR. Remote Sens. 2020, 12, 3453.

AMA Style

Pu L, Zhang X, Zhou Z, Shi J, Wei S, Zhou Y. A Phase Filtering Method with Scale Recurrent Networks for InSAR. Remote Sensing. 2020; 12(20):3453.

Chicago/Turabian Style

Pu, Liming; Zhang, Xiaoling; Zhou, Zenan; Shi, Jun; Wei, Shunjun; Zhou, Yuanyuan. 2020. "A Phase Filtering Method with Scale Recurrent Networks for InSAR" Remote Sens. 12, no. 20: 3453.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop