Complex-Valued Sparse Long Short-Term Memory Unit with Application to Super-Resolving SAR Tomography

Kun Qian, Yuanyuan Wang, Peter Jung, Yilei Shi, Xiao Xiang Zhu

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

To achieve super-resolution synthetic aperture radar (SAR) tomography (TomoSAR), compressive sensing (CS)-based algorithms are usually employed, which are, however, computationally expensive, and thus is not often applied in large-scale processing. Recently, deep unfolding techniques have provided a good combination of physical model-based algorithms and the ability of neural networks to learn from data. In this vein, iterative CS-based algorithms can usually be un-rolled as neural networks with only 10 to 20 layers. When trained, it shows great computational efficiency for further TomoSAR processing. However, the learning architecture of neural networks built in this approach tends to result in error propagation and information loss, thus degrading the performance. In this paper, we propose to employ complex-valued sparse long short-term memory (CV-SLSTM) units to tackle this problem by incorporating historically updating information into the optimization procedure and preserving full information. Simulations are carried out to validate the performance of the proposed algorithm.

OriginalspracheEnglisch
TitelIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten591-594
Seitenumfang4
ISBN (elektronisch)9781665427920
DOIs
PublikationsstatusVeröffentlicht - 2022
Veranstaltung2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia
Dauer: 17 Juli 202222 Juli 2022

Publikationsreihe

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Band2022-July

Konferenz

Konferenz2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Land/GebietMalaysia
OrtKuala Lumpur
Zeitraum17/07/2222/07/22

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