SAR imaging using a modern 2D spectral estimation method

Yadin, E.; Olmar, D.; Oron, Ori; Nathansohn, R.

doi:10.1109/radar.2008.4720775

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…It may represent pixel values of a 2D image, some other 2D spatial representation thereof (e.g., [7,19]), or a block ("aperture") within an image, with the estimated covariance matrix computed separately for each such block. Table 1 presents the taxonomy of the Covariance Method.…”

Section: The Covariance Methodsmentioning

confidence: 99%

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A computationally efficient algorithm for the 2D covariance method

Green

Birk

2013

Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

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The estimated covariance matrix is a building block for many algorithms, including signal and image processing. The Covariance Method is an estimator for the covariance matrix, favored both as an estimator and in view of the convenient properties of the matrix that it produces. However, the considerable computational requirements limit its use. We present a novel computation algorithm for the covariance method, which dramatically reduces the computational complexity (both ALU operations and memory access) relative to previous algorithms. It has a small memory footprint, is highly parallelizable and requires no synchronization among compute threads. On a 40-core X86 system, we achieve 1200X speedup relative to a straightforward singlecore implementation; even on a single core, 35X speedup is achieved.

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Section: The Covariance Methodsmentioning

confidence: 99%

“…Yadin et al [19] use images of size 8k × 8k with the MVM algorithm, computing 25 · 10 4 estimated covariance matrices (25X more matrices than were required by [7]). Both papers show that MVM gives a better output image than using FFT.…”

Section: Related Workmentioning

confidence: 99%

A computationally efficient algorithm for the 2D covariance method

Green

Birk

2013

Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

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“…As a result of this Fourier relationship, SAR may be viewed as a spectral estimation problem [3][4][5][6][7] . Classical Fourier methods of spectral estimation, such as the periodogram, are easy to understand and implement as well as computationally inexpensive, making them a good option for classical SAR systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient and accurate approximation of superresolution spectral estimation methods for SAR imaging

Batts,

Rigling

2024

Algorithms for Synthetic Aperture Radar Imagery XXXI

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Superresolution methods of spectral estimation have shown much promise for creating high quality SAR images that are devoid of the inherent shortcomings of classical Fourier techniques. However, many modern spectral estimation techniques involve the estimation of a correlation matrix and manipulations thereof, both of which can be prohibitively expensive. While efficient me thods of co mputing th ese sp ectral es timation te chniques have been explored in the literature, for large scenes with high resolution, it is often still too expensive to compute and store the data necessary for the computations. These techniques thus have had limited application in the vast array of SAR imaging problems. This paper presents several methods for generating accurate approximations of these spectral estimation techniques while further improving the computational efficiency an d re ducing the storage burden.

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“…As a result of this Fourier relationship, SAR may be viewed as a spectral estimation problem [6][7][8][9][10] . Classical Fourier methods of spectral estimation, such as the periodogram, are easy to understand and implement as well as computationally inexpensive, making them a good option for classical SAR systems.…”

Section: Introductionmentioning

confidence: 99%

Efficient computation of superresolution methods for SAR imaging

Batts,

Rigling

2023

Algorithms for Synthetic Aperture Radar Imagery XXX

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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.

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SAR imaging using a modern 2D spectral estimation method

Cited by 4 publications

References 2 publications

A computationally efficient algorithm for the 2D covariance method

A computationally efficient algorithm for the 2D covariance method

Efficient and accurate approximation of superresolution spectral estimation methods for SAR imaging

Efficient computation of superresolution methods for SAR imaging

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