Efficient computation of superresolution methods for SAR imaging

Alex Batts; Brian Rigling

doi:10.1117/12.2665913

13 June 2023 Efficient computation of superresolution methods for SAR imaging

Alex Batts, Brian Rigling

Proceedings Volume 12520, Algorithms for Synthetic Aperture Radar Imagery XXX; 1252008 (2023) https://doi.org/10.1117/12.2665913
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

While traditional Fourier methods of SAR imaging are well known in addition to being easy to implement, they have limitations in terms of quality, particularly with respect to speckle, scintillation, and side lobe artifacts. Methods of SAR imaging that have shown promise include superresolution methods like the Minimum Variance Method (MVM) and the Multiple Signal Classification (MUSIC) algorithm; however, these algorithms are computationally intense. Both algorithms require the estimation of a correlation matrix, and manipulations thereof, as well as computing the image spectrum through computation of a quadratic form for each image pixel. This paper presents an efficient method for estimating the correlation matrix and shows how the structure of the correlation matrix can be exploited to efficiently compute the aforementioned superresolution methods.

Conference Presentation

Citation Download Citation

Alex Batts and Brian Rigling "Efficient computation of superresolution methods for SAR imaging", Proc. SPIE 12520, Algorithms for Synthetic Aperture Radar Imagery XXX, 1252008 (13 June 2023); https://doi.org/10.1117/12.2665913

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