Estimation of the Equivalent Number of Looks in SAR Images Based on Singular Value Decomposition

Ren, Weilong; Song, Jianshe; Tian, Song; Zhang, Xiongmei

doi:10.1109/lgrs.2015.2457334

Cited by 13 publications

(4 citation statements)

References 10 publications

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“…Firstly, the singular matrix Θ Λ t is decomposed as Equation ( 23) by the singular value decomposition (SVD) [27] method:…”

Section: Sparse Imaging According To the Target-areasmentioning

confidence: 99%

Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Tian

et al. 2021

Remote Sensing

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Because of the three-dimensional (3D) imaging scene’s sparsity, compressed sensing (CS) algorithms can be used for linear array synthetic aperture radar (LASAR) 3D sparse imaging. CS algorithms usually achieve high-quality sparse imaging at the expense of computational efficiency. To solve this problem, a fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM) is proposed in this paper. The proposed method calculates the maximum marginal likelihood function under the framework of the RVM to obtain the optimal hyper-parameters; the scattering units corresponding to the non-zero optimal hyper-parameters are extracted as the target-areas in the imaging scene. Then, based on the target-areas, we simplify the measurement matrix and conduct sparse imaging. In addition, under low signal to noise ratio (SNR), low sampling rate, or high sparsity, the target-areas cannot always be extracted accurately, which probably contain several elements whose scattering coefficients are too small and closer to 0 compared to other elements. Those elements probably make the diagonal matrix singular and irreversible; the scattering coefficients cannot be estimated correctly. To solve this problem, the inverse matrix of the singular matrix is replaced with the generalized inverse matrix obtained by the truncated singular value decomposition (TSVD) algorithm to estimate the scattering coefficients correctly. Based on the rank of the singular matrix, those elements with small scattering coefficients are extracted and eliminated to obtain more accurate target-areas. Both simulation and experimental results show that the proposed method can improve the computational efficiency and imaging quality of LASAR 3D imaging compared with the state-of-the-art CS-based methods.

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“…Firstly, the singular matrix Θ Λ t is decomposed as Equation ( 23) by the singular value decomposition (SVD) [27] method:…”

Section: Sparse Imaging According To the Target-areasmentioning

confidence: 99%

Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Tian

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Assuming that estimates computed in windows only contain variation due to fully developed speckle, existing ENL estimators [41][42][43][44][45][46] are generally evaluated in sliding windows over the entire PolSAR images. Lee et al [41,42] proposed to produce a scatter plot of mean versus standard deviation of the intensity data in each sliding window, where the ENL can be inferred from its slope.…”

Section: Problem Statement and Previous Workmentioning

confidence: 99%

“…Joughin et al [44] proposed an ENL estimator, namely TM estimator, that originates from the second-order trace moment. Ren et al [45] proposed an ENL estimator for SAR imageries based on singular value decomposition. However, these unsupervised approaches are proposed assuming that an arbitrary window can share the same ENL value, which does not hold in complex scenarios due to presence of multiple classes in the same windows sampled from complex scenes.…”

Section: Problem Statement and Previous Workmentioning

confidence: 99%

Unsupervised Classification for Polarimetric SAR Data Using Variational Bayesian Wishart Mixture Model with Inverse Gamma-Gamma Prior

Ren,

Zhou,

Wang

2021

Preprint

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Although various clustering methods have been successfully applied to polarimetric synthetic aperture radar (PolSAR) image clustering tasks, most of the available approaches fail to realize automatic determination of cluster number, nor have they derived an exact distribution for the number of looks. To overcome these limitations and achieve robust unsupervised classification of PolSAR images, this paper proposes the variational Bayesian Wishart mixture model (VB-WMM), where variational Bayesian expectation maximization (VBEM) technique is applied to estimate the variational posterior distribution of model parameters iteratively. Besides, covariance matrix similarity and geometric similarity are combined to incorporate spatial information of PolSAR images. Furthermore, we derive a new distribution named inverse gamma-gamma (IGG) prior that originates from the log-likelihood function of proposed model to enable efficient handling of number of looks. As a result, we obtain a closed-form variational lower bound, which can be used to evaluate the convergence of proposed model. We validate the superiority of proposed method in clustering performance on four real-measured datasets and demonstrate significant improvements towards conventional methods. As a by-product, the experiments show that our proposed IGG prior is effective in estimating the number of looks.

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“…This paper proposes a traveling wave-ranging scheme for arc faults in DC distribution networks. It is based on the combination of empirical modal decomposition (EMD) [22] and singular value decomposition (SVD) [23]. The empirical modal decomposition method is used to decompose the fault current traveling wave on the line and obtain a series of intrinsic modal functions (IMFs) to extract the highest frequency component.…”

Section: Introductionmentioning

confidence: 99%

An Improved Arc Fault Location Method of DC Distribution System Based on EMD-SVD Decomposition

et al. 2023

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The influence of the control strategy of the power electronic converter obscures the fault characteristics of DC distribution networks. The existence of arc faults over an extended period of time poses a grave threat to the security of power grids and may result in electric shock, fire, and other catastrophes. In recent years, the method of fault localization based on the traveling wave method has been a popular topic of research in the field of DC distribution system protection. In this paper, the fault localization principle of the traveling wave method is described in depth, and the propagation characteristics of the traveling wave of fault current in the online mode network are deduced. We present a method for wave head calibration that combines empirical mode decomposition (EMD) and singular value decomposition (VMD). After the fault-traveling current signal has been subjected to EMD, the first eigenmode function is extracted and subjected to singular value decomposition (SVD). After SVD, the detail component can reflect the singularity of the signal. The point of the maximum value of the detail component signal corresponds to the moment when the faulty traveling wave head reaches the monitoring point. Finally, the DC distribution system is modeled based on the PSCAD/EMTDC simulation environment, and the fault location method is verified. The simulation results show that the method can effectively realize fault localization.

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Estimation of the Equivalent Number of Looks in SAR Images Based on Singular Value Decomposition

Cited by 13 publications

References 10 publications

Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Unsupervised Classification for Polarimetric SAR Data Using Variational Bayesian Wishart Mixture Model with Inverse Gamma-Gamma Prior

An Improved Arc Fault Location Method of DC Distribution System Based on EMD-SVD Decomposition

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