Sparse representation-based synthetic aperture radar imaging

Samadi, Sadegh; Çetin, Müjdat; Masnadi-Shirazi, Mohammad Ali

doi:10.1049/iet-rsn.2009.0235

Cited by 133 publications

(111 citation statements)

References 21 publications

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“…The resurgent development of many theoretical analysis frameworks [22,23] and effective algorithms [24] has been witnessed. The applications of the sparse signal representation technique mainly include radar imaging [25,26], image restoration [27], image classification [28,29], and pattern recognition [15,30]. The key of sparse signal model is based on the fact that a certain signal can be represented by an overcomplete basis set (dictionary).…”

Section: Srcmentioning

confidence: 99%

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

et al. 2018

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Abstract:The monogenic signal, which is defined as a linear combination of a signal and its Riesz-transformed one, provides a great opportunity for synthetic aperture radar (SAR) image recognition. However, the incredibly large number of components at different scales may result in too much of a burden for onboard computation. There is great information redundancy in monogenic signals because components at some scales are less discriminative or even have negative impact on classification. In addition, the heterogeneity of the three types of components will lower the quality of decision-making. To solve the problems above, a scale selection method, based on a weighted multi-task joint sparse representation, is proposed. A scale selection model is designed and the Fisher score is presented to measure the discriminative ability of components at each scale. The components with high Fisher scores are concatenated to three component-specific features, and an overcomplete dictionary is built. Meanwhile, the scale selection model produces the weight vector. The three component-specific features are then fed into a multi-task joint sparse representation classification framework. The final decision is made in terms of accumulated weighted reconstruction error. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset have proved the effectiveness and superiority of our method.

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Section: Srcmentioning

confidence: 99%

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

et al. 2018

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“…This could lead to a sparser representation for scenes exhibiting different types of features at different spatial locations. Based on these thoughts, a synthesis model for sparsity-driven SAR imaging has been proposed in [9]. As in (3), what admits sparse representation is the magnitude of the reflectivity field s. Hence we are interested in a representation of the form |s| = Dα, where D is an overcomplete dictionary with the coefficient vector α.…”

Section: Analysis and Synthesis-based Sparse Reconstruction For Sarmentioning

confidence: 99%

Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

Çetin

Stojanović

Önhon

et al. 2014

IEEE Signal Process. Mag.

252

125

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This paper presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging.In particular, it reviews (i) analysis and synthesis-based sparse signal representation formulations for SAR image formation together with the associated imaging results; (ii) sparsity-based methods for wide-angle SAR imaging and anisotropy characterization; (iii) sparsity-based methods for joint imaging and autofocusing from data with phase errors; (iv) techniques for exploiting sparsity for SAR imaging of scenes containing moving objects, and (v) recent work on compressed sensing-based analysis and design of SAR sensing missions.

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“…Since the true 3-D images of the underlying scenes are not available in our experiments, here we use two quality metrics, the target-tobackground ratio (TBR) and the entropy of image (ENT) [36], to quantitatively evaluate the performance of the recovered images.…”

Section: The Performance Metricsmentioning

confidence: 99%

Sparse Array Microwave 3-D Imaging: Compressed Sensing Recovery and Experimental Study

Wei¹,

Zhang²,

Shi³

et al. 2013

PIER

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Abstract-Microwave array 3-D imaging is an emerging technique capable of producing a 3-D map of scattered electric fields. Its all-weather and large scene imaging features make it an attractive powerful tool for target detection and feature extraction. Typically, a microwave array 3-D imaging system based on the classical sampling theory requires a large dense 2-D antenna array, which may suffer from a very high cost. To reduce the number of the antenna array elements, this paper surveys the use of compressed sensing recovery and sparse measurement strategies for microwave array 3-D imaging. Combining with the typical spatial sparsity of the underlying scene, we pose the sparse array microwave 3-D imaging as finding sparse solutions to under-determined linear equations. Further, to reduce the computational of the compressed sensing recovery with the largescale echoes data, we divide the underlying 3-D scene into a series of equal-range 2-D slices, and deal with these slices separately using the orthogonal matching pursuit (OMP) algorithm. Lastly, the performance of the presented compressed sensing approach is verified by an X-band microwave array 3-D imaging system. The experimental results demonstrate that the compressed sensing approach can produce a better resolution 3-D image of the observed scatterers compared with the conventional method, especially in the case of very sparse activate antenna array.

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Sparse representation-based synthetic aperture radar imaging

Cited by 133 publications

References 21 publications

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

Sparse Array Microwave 3-D Imaging: Compressed Sensing Recovery and Experimental Study

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