RPC Estimation via $\ell_1$-Norm-Regularized Least Squares (L1LS)

Long, Tao; Jiao, Weili; He, Guojin

doi:10.1109/tgrs.2015.2401602

Cited by 47 publications

(18 citation statements)

References 29 publications

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“…In addition, a phase correlation-based method (called the PC method) was also applied to estimate the displacement of each tile pair instead of the similarity transform. Lastly, a terrain-dependent RPC model [49] fitted to all the final control points, combined with the random sampling consensus algorithm (RANSAC), was utilized to correct the whole image. After rectification, fifty checkpoints, distributed evenly in the image, were selected manually to validate the final registration accuracy in terms of three measurements, e x , e y and e, the same as in the experiment where the similarity transform was estimated directly Secstion 4.2.3.1.…”

Section: Register Scenes Of Remote Sensing By Tilingmentioning

confidence: 99%

An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform

et al. 2018

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Image registration is a core technology of many different image processing areas and is widely used in the remote sensing community. The accuracy of image registration largely determines the effect of subsequent applications. In recent years, phase correlation-based image registration has drawn much attention because of its high accuracy and efficiency as well as its robustness to gray difference and even slight changes in content. Many researchers have reported that the phase correlation method can acquire a sub-pixel accuracy of 1/10 or even 1/100. However, its performance is acquired only in the case of translation, which limits the scope of the application of the method. However, there are few reports on the estimation of scales and angles based on the phase correlation method. To take advantage of the high accuracy property and other merits of phase correlation-based image registration and extend it to estimate the similarity transform, we proposed a novel algorithm, the Multilayer Polar Fourier Transform (MPFT), which uses a fast and accurate polar Fourier transform with different scaling factors to calculate the log-polar Fourier transform. The structure of the polar grids of MPFT is more similar to the one of the log-polar grid. In particular, for rotation estimation only, the polar grid of MPFT is the calculation grid. To validate its effectiveness and high accuracy in estimating angles and scales, both qualitative and quantitative experiments were carried out. The quantitative experiments included a numerical simulation as well as synthetic and real data experiments. The experimental results showed that the proposed method, MPFT, performs better than the existing phase correlation-based similarity transform estimation methods, the Pseudo-polar Fourier Transform (PPFT) and the Multilayer Fractional Fourier Transform method (MLFFT), and the classical feature-based registration method, Scale-Invariant Feature Transform (SIFT), and its variant, ms-SIFT.

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Section: Register Scenes Of Remote Sensing By Tilingmentioning

confidence: 99%

An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform

et al. 2018

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“…Ключевые слова: метод согласованного оценивания, RFM-модель Введение RFM-модель устанавливает математическую связь между геодезическими координатами объекта и его координатами на изображении с помощью коэффициентов многочленов, которые называются коэффициентами рационального многочлена (RPC -Rational Polynomial Coefficients). RPC-коэффициенты наиболее популярный способ описания модели сенсора [1]. Для нахождения коэффициентов модели обычно используют метод наименьших квадратов (МНК) [2].…”

Section: аннотацияunclassified

“…Рациональным путем нарастания могущества нашей страны являются развитие, сохранение, а также мобилизация интеллектуального потенциала России. Будущее нашей страны тесно взаимосвязано с переходом на инновационный путь развития, при этом ключевую роль реализации намеченного пути выполняет образование [1]. В современных условиях одним из основных показателей реализации образовательной политики вуза является конкурентоспособность университета, а также его рейтинг в числе других профильных высших учебных учреждений.…”

Section: аннотацияunclassified

Constructing consistent estimates of survey model parameters using reduced dimension support system

Kotov¹

2018

Тенденции Развития Науки И Образования

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Котов А.П. Построение согласованных оценок параметров модели съемки с использованием вспомогательной системы уменьшенной размерности Самарский национальный исследовательский университет им. академика С.П. Королева

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“…For regularization, there are many sparsity regularizers for relaxing the 0 -norm, among which the convex 1 -norm [14,15] and the nonconvex -norm to the -th power [16][17][18] are the main methods:…”

Section: Introductionmentioning

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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RPC Estimation via $\ell_1$-Norm-Regularized Least Squares (L1LS)

Cited by 47 publications

References 29 publications

An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform

An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform

Constructing consistent estimates of survey model parameters using reduced dimension support system

A New Smoothed L0 Regularization Approach for Sparse Signal Recovery

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