Compressive sensing-based sparse representation analysis for remote sensing SAR imagery

Hang Yin; Jue Wang; Xinyi Wen; Xinran Xie

doi:10.1117/12.3048870

27 November 2024 Compressive sensing-based sparse representation analysis for remote sensing SAR imagery

Hang Yin, Jue Wang, Xinyi Wen, Xinran Xie

Proceedings Volume 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024); 1340208 (2024) https://doi.org/10.1117/12.3048870
Event: International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 2024, Zhengzhou, China

Abstract

The signal processing technology of Compressive Sensing (CS) overturns the limitation of the traditional sensing method that only high sampling rate can achieve high resolution. The compressive sensing method saves internal storage and resources of information processing due to the small amount of data processed. Applying CS to spaceborne Synthetic Aperture Radar (SAR) system can effectively reduce the pressure of hardware platform. Sparse representation of SAR image is a prerequisite for CS application in remote sensing SAR imagery, which is analyzed quantitatively under different orthogonal bases and representation approach. The effect analysis is measured by the evaluation indexes such as peak signal-to-noise ratio, structural similarity and edge retention index. The simulation results show that the effect of sparse representation can meet the quality and structure requirements of SAR images in most cases. The conclusion verifies that SAR images satisfy CS requirement for sparsity, which supports the further study of CS reconstruction application in spaceborne SAR image.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hang Yin, Jue Wang, Xinyi Wen, and Xinran Xie "Compressive sensing-based sparse representation analysis for remote sensing SAR imagery", Proc. SPIE 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 1340208 (27 November 2024); https://doi.org/10.1117/12.3048870

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Synthetic aperture radar

Associative arrays

Discrete wavelet transforms

Image quality

Compressed sensing

Remote sensing

Signal processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years