&lt;title&gt;SAR imaging via modern 2D spectral estimation methods&lt;/title&gt;

DeGraaf, S.

doi:10.1117/12.177188

Cited by 16 publications

(15 citation statements)

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“…It is clear from the relative phase plot in Figure 3 that processing the two images independently degrades the inter-channel information, compared with the polar format algorithm images in Figure 2. The relative phase degradation would result in a similar degradation for scattering center height estimation using equation (4). Figure 4 shows the resulting image and relative phases for the jointly-enhanced image using the procedure in Section 3.…”

Section: Simulation Resultsmentioning

confidence: 91%

“…2 To address these resolution limits and artifact terms, nonlinear reconstruction techniques have been proposed. [3][4][5] In this paper we focus on a sparse-signal enhancement approach. 2,3 The basic idea is to reconstruct an image that is simultaneously in good agreement with the measured data, and is regularized by using some prior information; the most common prior information is that the reconstruction is sparse in some domain.…”

Section: Introductionmentioning

confidence: 99%

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Joint enhancement of multichannel SAR data

2007

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In this paper we consider the problem of joint enhancement of multichannel Synthetic Aperture Radar (SAR) data. Previous work by Cetin and Karl introduced nonquadratic regularization methods for image enhancement using sparsity enforcing penalty terms. For multichannel data, independent enhancement of each channel is shown to degrade the relative phase information across channels that is useful for 3D reconstruction. We thus propose a method for joint enhancement of multichannel SAR data with joint sparsity constraints. We develop both a gradient-based and a Lagrange-Newton-based method for solving the joint reconstruction problem, and demonstrate the performance of the proposed methods on IFSAR height extraction problem from multi-elevation data.

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Section: Simulation Resultsmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Joint enhancement of multichannel SAR data

2007

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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%

“…It has also been applied elsewhere: signal processing applications wherein the signal's temporal correlation is needed [7], synthetic aperture radar range-azimuth focusing [15], smoothing spatial clutter by averaging over given transposed data [8], to perform 2D spectral analysis [11], and more. In all the aforementioned cases, the generation of the estimated covariance matrix using the Covariance Method is an important computational building block.…”

Section: Introductionmentioning

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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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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“…An extended analysis of the various spectral estimation methods that can be used is given, from a signal processing view in [13], and applied to SAR imaging in [14]. However, most of the methods require an a priori knowledge or estimation of the number of scatterers, which deeply limits their application to SAR imaging and features extraction.…”

Section: Clean/relax Algorithms and High Resolution Bright Points Extmentioning

confidence: 99%