Smoothing for continuous dynamical state space models with sampled system coefficients based on sparse kernel learning

Qian, Nijia; Chang, Guobin; Gao, Jingxiang

doi:10.1007/s11071-020-05698-0

Cited by 11 publications

(4 citation statements)

References 53 publications

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“…Finally, FCM clustering algorithm was put into use to produce the binary change map. What is more, EM [57] and K-Means [58] were adopted to be compared with FCM for the purpose of verifying the usability of LSSC. CVA was conducted in the contrast experiment.…”

Section: Resultsmentioning

confidence: 99%

Unsupervised Change Detection Using Spectrum-Trend and Shape Similarity Measure

2020

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The emergence of very high resolution (VHR) images contributes to big challenges in change detection. It is hard for traditional pixel-level approaches to achieve satisfying performance due to radiometric difference. This work proposes a novel feature descriptor that is based on spectrum-trend and shape context for VHR remote sensing images. The proposed method is mainly composed of two aspects. The spectrum-trend graph is generated first, and then the shape context is applied in order to describe the shape of spectrum-trend. By constructing spectrum-trend graph, spatial and spectral information is integrated effectively. The approach is performed and assessed by QuickBird and SPOT-5 satellite images. The quantitative analysis of comparative experiments proves the effectiveness of the proposed technique in dealing with the radiometric difference and improving the accuracy of change detection. The results indicate that the overall accuracy and robustness are both boosted. Moreover, this work provides a novel viewpoint for discriminating changed and unchanged pixels by comparing the shape similarity of local spectrum-trend.

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Section: Resultsmentioning

confidence: 99%

Unsupervised Change Detection Using Spectrum-Trend and Shape Similarity Measure

2020

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“…In order to verify the performance of the wind speed estimation model, the estimated wind speed was compared with ERA-5 reanalysis data of ECMWF. RMSE, mean absolute error (MAE), Pearson correlation coefficient, and mean absolute percentage error (MAPE) were used to evaluate the performance of wind speed estimated by the models [51]. Their calculation formulas are as follows:…”

Section: Assessment Methodsmentioning

confidence: 99%

Developing and Testing Models for Sea Surface Wind Speed Estimation with GNSS-R Delay Doppler Maps and Delay Waveforms

Zhu

et al. 2020

Remote Sensing

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This paper focuses on sea surface wind speed estimation based on cyclone global navigation satellite system reflectometry (GNSS-R) data. In order to extract useful information from delay-Doppler map (DDM) data, three delay waveforms are presented for wind speed estimation. The delay waveform without Doppler shift is defined as central delay waveform (CDW), and the integral of the delay waveforms with different Doppler shift values is defined as integral delay waveform (IDW), while the difference between normalized IDW (NIDW) and normalized CDW (NCDW) is defined as differential delay waveform (DDW). We first propose a data filtering method based on threshold setting for data quality control. This method can select good-quality DDM data by adjusting the root mean square (RMS) threshold of cleaned DDW. Then, the normalized bistatic radar scattering cross section (NBRCS) and the leading edge slope (LES) of IDW are calculated using clean DDM data. Wind speed estimation models based on NBRCS and LES observations are then developed, respectively, and on this basis, a combination wind speed estimation model based on determination coefficient is further proposed. The CYGNSS data and ECMWF reanalysis data collected from 12 May 2020 to 12 August 2020 are used, excluding data collected on land, to evaluate the proposed models. The evaluation results show that the wind speed estimation accuracy of the piecewise function model based on NBRCS is 2.3 m/s in terms of root mean square error (RMSE), while that of the double-parameter and triple-parameter models is 2.6 and 2.7 m/s, respectively. The wind speed estimation accuracy of the double-parameter and triple-parameter models based on LES is 3.3 and 2.5 m/s. The results also demonstrate that the RMSE of the combination method is 2.1 m/s, and the coefficient of determination is 0.906, achieving a considerable performance gain compared with the individual NBRCS- and LES-based methods.

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“…In this work, a highly efficient method, called FISTA, is employed [26]. This algorithm has also been used in our previous studies [28][29][30]. It is a proximal gradient algorithm, which is further accelerated by the Nesterov momentum method [31].…”

Section: Computation Algorithmmentioning

confidence: 99%

Sparse DDK: A Data-Driven Decorrelation Filter for GRACE Level-2 Products

et al. 2022

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High-frequency and correlated noise filtering is one of the important preprocessing steps for GRACE level-2 products before calculating mass anomaly. Decorrelation and denoising kernel (DDK) filters are usually considered as such optimal filters to solve this problem. In this work, a sparse DDK filter is proposed. This is achieved by replacing Tikhonov regularization in traditional DDK filters with weighted L1 norm regularization. The proposed sparse DDK filter adopts a time-varying error covariance matrix, while the equivalent signal covariance matrix is adaptively determined by the Gravity Recovery and Climate Experiment (GRACE) monthly solution. The covariance matrix of the sparse DDK filtered solution is also developed from the Bayesian and error-propagation perspectives, respectively. Furthermore, we also compare and discuss the properties of different filters. The proposed sparse DDK has all the advantages of traditional filters, such as time-varying, location inhomogeneity, and anisotropy, etc. In addition, the filtered solution is sparse; that is, some high-degree and high-order terms are strictly zeros. This sparsity is beneficial in the following sense: high-degree and high-order sparsity mean that the dominating noise in high-degree and high-order terms is completely suppressed, at a slight cost that the tiny signals of these terms are also discarded. The Center for Space Research (CSR) GRACE monthly solutions and their error covariance matrices, from January 2004 to December 2010, are used to test the performance of the proposed sparse DDK filter. The results show that the sparse DDK can effectively decorrelate and denoise these data.

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Smoothing for continuous dynamical state space models with sampled system coefficients based on sparse kernel learning

Cited by 11 publications

References 53 publications

Unsupervised Change Detection Using Spectrum-Trend and Shape Similarity Measure

Unsupervised Change Detection Using Spectrum-Trend and Shape Similarity Measure

Developing and Testing Models for Sea Surface Wind Speed Estimation with GNSS-R Delay Doppler Maps and Delay Waveforms

Sparse DDK: A Data-Driven Decorrelation Filter for GRACE Level-2 Products

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