Compressive SAR Imaging with Joint Sparsity and Local Similarity Exploitation

Shen, Fangfang; Zhao, Guanghui; Shi, Guangming; Dong, Weisheng; Wang, Chenglong; Niu, Yi

doi:10.3390/s150204176

Cited by 7 publications

(9 citation statements)

References 19 publications

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“…According to CS theory [ 21 , 22 , 23 , 24 ], sparse signals can be recovered from samples taken at a lower sampling rate than Nyquist criterion; e.g., the spatial domain samples can be spaced at more than λ min /2 = c m /(2 f max ).…”

Section: Compressed Sensing Techniquementioning

confidence: 99%

Compressed Sensing Techniques Applied to Ultrasonic Imaging of Cargo Containers

Álvarez-López

Lorenzo

2017

Sensors

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One of the key issues in the fight against the smuggling of goods has been the development of scanners for cargo inspection. X-ray-based radiographic system scanners are the most developed sensing modality. However, they are costly and use bulky sources that emit hazardous, ionizing radiation. Aiming to improve the probability of threat detection, an ultrasonic-based technique, capable of detecting the footprint of metallic containers or compartments concealed within the metallic structure of the inspected cargo, has been proposed. The system consists of an array of acoustic transceivers that is attached to the metallic structure-under-inspection, creating a guided acoustic Lamb wave. Reflections due to discontinuities are detected in the images, provided by an imaging algorithm. Taking into consideration that the majority of those images are sparse, this contribution analyzes the application of Compressed Sensing (CS) techniques in order to reduce the amount of measurements needed, thus achieving faster scanning, without compromising the detection capabilities of the system. A parametric study of the image quality, as a function of the samples needed in spatial and frequency domains, is presented, as well as the dependence on the sampling pattern. For this purpose, realistic cargo inspection scenarios have been simulated.

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Section: Compressed Sensing Techniquementioning

confidence: 99%

Compressed Sensing Techniques Applied to Ultrasonic Imaging of Cargo Containers

Álvarez-López

Lorenzo

2017

Sensors

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“…The premise of the work is the assumption derived from SAIR image statistics that the RRIA model holds RIP which has been proved efficient by CS-based SAIR imaging. To further use SAIR images statistics, with the reweighted L 1 model, RRIA employs prior information estimated by the energy functionals of SAIR images as a constraint condition to make the algorithm more robust [12,13]. The experimental results demonstrate that the RRIA enhances the sparsity and reduces anomalous values of recovered signals significantly compared with unweighted L 1 -minimization.…”

Section: Introductionmentioning

confidence: 99%

A Robust Reweighted L1-Minimization Imaging Algorithm for Passive Millimeter Wave SAIR in Near Field

Zhang

Zhu

et al. 2015

Sensors

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The Compressive Sensing (CS) approach has proven to be useful for Synthetic Aperture Interferometric Radiometer (SAIR) imaging because it provides the same high-resolution capability while using part of interferometric observations compared to traditional methods using the entirety. However, it cannot always obtain the sparsest solution and may yield outliers with the non-adaptive random measurement matrix adopted by current CS models. To solve those problems, this paper proposes a robust reweighted L1-minimization imaging algorithm, called RRIA, to reconstruct images accurately by combining the sparsity and prior information of SAIR images in near field. RRIA employs iterative reweighted L1-minimization to enhance the sparsity to reconstruct SAIR images by computing a new weight factor in each iteration according to the previous SAIR images. Prior information estimated by the energy functional of SAIR images is introduced to RRIA as an additional constraint condition to make the algorithm more robust for different complex scenes. Compared to the current basic CS approach, our simulation results indicate that RRIA can achieve better recovery with the same amount of interferometric observations. Experimental results of different scenes demonstrate the validity and robustness of RRIA.

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“…According to CS theory [21][22][23][24], sparse signals can be recovered from samples taken at a lower sampling rate than Nyquist criterion; e.g., the spatial domain samples can be spaced at more than λ min /2 = c m /(2f max ).…”

Section: Compressed Sensing Techniquementioning

confidence: 99%

“…To fulfill the RIP, the sensing matrix Φ is generally a binary random matrix [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. In this situation, N sub out of N positions arranged in a Nx × Nf matrix are selected.…”

Section: Analysis Of the Sampling Schemesmentioning

confidence: 99%

Compressed Sensing Techniques for Ultrasonic Imaging of Cargo Containers

Lorenzo

Álvarez-López

2016

Volume 9: Mechanics of Solids, Structures and Fluids; NDE, Diagnosis, and Prognosis

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Abstract:One of the key issues in the fight against the smuggling of goods has been the development of scanners for cargo inspection. X-ray-based radiographic system scanners are the most developed sensing modality. However, they are costly and use bulky sources that emit hazardous, ionizing radiation. Aiming to improve the probability of threat detection, an ultrasonic-based technique, capable of detecting the footprint of metallic containers or compartments concealed within the metallic structure of the inspected cargo, has been proposed. The system consists of an array of acoustic transceivers that is attached to the metallic structure-under-inspection, creating a guided acoustic Lamb wave. Reflections due to discontinuities are detected in the images, provided by an imaging algorithm. Taking into consideration that the majority of those images are sparse, this contribution analyzes the application of Compressed Sensing (CS) techniques in order to reduce the amount of measurements needed, thus achieving faster scanning, without compromising the detection capabilities of the system. A parametric study of the image quality, as a function of the samples needed in spatial and frequency domains, is presented, as well as the dependence on the sampling pattern. For this purpose, realistic cargo inspection scenarios have been simulated.

show abstract

Compressive SAR Imaging with Joint Sparsity and Local Similarity Exploitation

Cited by 7 publications

References 19 publications

Compressed Sensing Techniques Applied to Ultrasonic Imaging of Cargo Containers

Compressed Sensing Techniques Applied to Ultrasonic Imaging of Cargo Containers

A Robust Reweighted L1-Minimization Imaging Algorithm for Passive Millimeter Wave SAIR in Near Field

Compressed Sensing Techniques for Ultrasonic Imaging of Cargo Containers

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