Compressed Sensing SAR Imaging Based on Centralized Sparse Representation

Ni, Jiacheng; Zhang, Qun; Luo, Ying; Li, Sun

doi:10.1109/jsen.2018.2831921

Cited by 41 publications

(21 citation statements)

References 28 publications

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“…Sparse signal recovery experiments on both the simulated signal and real images show the proposed RRSL0 algorithm performs better than the 1 or regularization methods and classical 0 regularization methods. In addition, we would also like to apply the proposed algorithm to other CS applications such as the RPCA [36,37] and SAR imaging [38].…”

Section: Discussionmentioning

confidence: 99%

A New Smoothed L0 Regularization Approach for Sparse Signal Recovery

Xiang

Yue

Wang

2019

Mathematical Problems in Engineering

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Sparse signal reconstruction, as the main link of compressive sensing (CS) theory, has attracted extensive attention in recent years. The essence of sparse signal reconstruction is how to recover the original signal accurately and effectively from an underdetermined linear system equation (ULSE). For this problem, we propose a new algorithm called regularization reweighted smoothed L0 norm minimization algorithm, which is simply called RRSL0 algorithm. Three innovations are made under the framework of this method: (1) a new smoothed function called compound inverse proportional function (CIPF) is proposed; (2) a new reweighted function is proposed; and (3) a mixed conjugate gradient (MCG) method is proposed. In this algorithm, the reweighted function and the new smoothed function are combined as the sparsity promoting objective, and the constraint condition y-Φx22 is taken as a deviation term. Both of them constitute an unconstrained optimization problem under the Tikhonov regularization criterion and the MCG method constructed is used to optimize the problem and realize high-precision reconstruction of sparse signals under noise conditions. Sparse signal recovery experiments on both the simulated and real data show the proposed RRSL0 algorithm performs better than other popular approaches and achieves state-of-the-art performances in signal and image processing.

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Section: Discussionmentioning

confidence: 99%

A New Smoothed L0 Regularization Approach for Sparse Signal Recovery

Xiang

Yue

Wang

2019

Mathematical Problems in Engineering

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“…Different approaches exist that can be used to form the image of the pipe such as back-projection algorithm [25], ω − k algorithm [3], beamforming [26], compressed sensing [27], and MUSIC-LSE [12]. However, as we are operating within the near-field region where a tiny defect needs to be detected on the reconstructed pipe image under insulation, the acquired SAR raw data need to be pre-processed and refocused before the reconstructing the pipe image.…”

Section: A Design Of Microwave Ndt Based Sar Systemmentioning

confidence: 99%

Microwave-Based SAR Technique for Pipeline Inspection Using Autofocus Range-Doppler Algorithm

2019

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In non-destructive testing and evaluation, microwave-based synthetic aperture radar (SAR) imaging have shown great potential in the detection of defects on buried objects such as pipes. However, due to pipe curvature and high standoff distance when inspecting an insulated pipe, the useful defect information used to characterise the pipe image is lost as a result of low signal-to-noise ratio (SNR) resulting in a blurred and unfocused image. In this paper, we proposed a robust microwave-based SAR imaging using autofocus range-Doppler algorithm (RDA) for the inspection of an insulated pipe. Singular value decomposition (SVD) is used to mitigate the effect of the insulation layer by removing dominant singular values that characterise the insulation layer, and the autofocus RDA is designed to refocus the SAR image using RDA residual refocusing. SNR, improvement factor (IF) and squared error (SE) are used to evaluate the qualitative image information of the defect on the pipe. Experimental results showed the efficacy of the method in detecting defects on an insulated pipe, in particular, a significant reduction in the noise content of the image compared to the known SAR Omega-k algorithm. It was found that the autofocus RDA gave higher values of SNR and IF (3 dB and 6 dB) compared to the Omega-k algorithm (-1 dB and 2 dB) respectively.

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“…1. The number of samples for winter wheat-summer maize, winter wheat-summer peanut, winter wheat-summer cotton, winter wheat-others, spring maize, spring peanut, spring cotton, and others are 108, 16,30,32,24,28,26, and 154, respectively. The 50% of the samples were selected as training samples for the generation of the dictionary, and the others preserved for estimating classification accuracy.…”

Section: B Data Description and Pre-processing 1) Modis Data And Prementioning

confidence: 99%

“…In recent years, sparse representation has shown promising performance in many applications, such as face recognition [23], target detection [24], image fusion [25], image compression [26], image classification [27], and others [28]. From the theory of sparse representation, an identified sample is assumed to be approximately represented by a linear combination of as few atoms as possible of a given dictionary.…”

Section: Introductionmentioning

confidence: 99%

Crop Area Identification Based on Time Series EVI2 and Sparse Representation Approach: A Case Study in Shandong Province, China

et al. 2019

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The accurate and timely spatial distribution information of various crop types is vital for food security. In this study, the 2-band enhanced vegetation index (EVI2) data from Moderate Resolution Imaging Spectroradiometer (MODIS) were combined with sparse representation approach to identify the distribution of various crop types in Shandong Province, China. Three groups of input variables derived from EVI2 including annual time series EVI2 (TS), harmonic features (HF), and combined vector formed by harmonic features and texture features (HFT) were used. The online dictionary learning and orthogonal matching pursuit algorithms were applied to generate the dictionary and solve the sparse coefficients of the identified samples, respectively. Then, the label of the identified samples can be obtained according to the minimum residuals between the dictionary and the sparse coefficients of the identified samples. At the provincial level, the validation based on the statistical data showed that three groups of input variables presented lower than ±25% at relative errors, and input variables of HFT performed better than the other two. At the municipal level, the results achieved by using input variables of HFT also agreed well with the statistics with the coefficient of determination R 2 > 0.85 for wheat and maize, as well as R 2 > 0.71 for peanut and cotton during 2014-2016. These results demonstrate that the combination of the input variables of HFT derived from time series MODIS EVI2 data and sparse representation approach can be used for crop identification in the study area. INDEX TERMS Crop area identification, harmonic features, sparse representation, texture features, time series EVI2.

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Compressed Sensing SAR Imaging Based on Centralized Sparse Representation

Cited by 41 publications

References 28 publications

A New Smoothed L0 Regularization Approach for Sparse Signal Recovery

A New Smoothed L0 Regularization Approach for Sparse Signal Recovery

Microwave-Based SAR Technique for Pipeline Inspection Using Autofocus Range-Doppler Algorithm

Crop Area Identification Based on Time Series EVI2 and Sparse Representation Approach: A Case Study in Shandong Province, China

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