scholarly journals Assessment of Wind Direction Estimation Methods from SAR Images

2020 ◽  
Vol 12 (21) ◽  
pp. 3631
Author(s):  
Alexandre Corazza ◽  
Ali Khenchaf ◽  
Fabrice Comblet
Keyword(s):  
High Resolution ◽  
Simulation Study ◽  
Coastal Area ◽  
Wind Wave ◽  
Estimation Methods ◽  
Wave Direction ◽  
Sar Images ◽  
Offshore Area ◽  
Direction Estimation ◽  
Field Estimation

Wind information on SAR images are essential to characterize a marine environment in offshore or coastal area. More and more applications require high resolution wind field estimation. In this article, classical wind wave direction estimation methods are reviewed as the spectral or gradient approaches. In addition, a way to enhance the spectral method with the Radon transform is proposed. The aim of this document is to determine which method provides greatest results when the resolution grid is finer. Therefore, the methods accuracy, fidelity and uncertainty are compared through a simulation study, a section with RadarSAT2 data in coastal area and another one with Sentinel-1 measurements in offshore area.

scholarly journals Multi-Scene Building Height Estimation Method Based on Shadow in High Resolution Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 2862
Author(s):  
Yakun Xie ◽  
Dejun Feng ◽  
Sifan Xiong ◽  
Jun Zhu ◽  
Yangge Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Estimation Method ◽  
Absolute Error ◽  
Large Error ◽  
Estimation Methods ◽  
Height Estimation ◽  
Building Height ◽  
Remote Sensing Imagery ◽  
High Resolution Imagery

Accurately building height estimation from remote sensing imagery is an important and challenging task. However, the existing shadow-based building height estimation methods have large errors due to the complex environment in remote sensing imagery. In this paper, we propose a multi-scene building height estimation method based on shadow in high resolution imagery. First, the shadow of building is classified and described by analyzing the features of building shadow in remote sensing imagery. Second, a variety of shadow-based building height estimation models is established in different scenes. In addition, a method of shadow regularization extraction is proposed, which can solve the problem of mutual adhesion shadows in dense building areas effectively. Finally, we propose a method for shadow length calculation combines with the fish net and the pauta criterion, which means that the large error caused by the complex shape of building shadow can be avoided. Multi-scene areas are selected for experimental analysis to prove the validity of our method. The experiment results show that the accuracy rate is as high as 96% within 2 m of absolute error of our method. In addition, we compared our proposed approach with the existing methods, and the results show that the absolute error of our method are reduced by 1.24 m-3.76 m, which can achieve high-precision estimation of building height.

Evaluating a Computerized Adaptive Testing Version of a Cognitive Ability Test Using a Simulation Study

2021 ◽  
pp. 073428292110277
Author(s):  
Ioannis Tsaousis ◽  
Georgios D. Sideridis ◽  
Hannan M. AlGhamdi
Keyword(s):  
Cognitive Ability ◽  
Simulation Study ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Estimation Methods ◽  
Item Pool ◽  
Sequential Approach ◽  
Ability Test ◽  
Promising Alternative ◽  
Item Exposure

This study evaluated the psychometric quality of a computerized adaptive testing (CAT) version of the general cognitive ability test (GCAT), using a simulation study protocol put forth by Han, K. T. (2018a). For the needs of the analysis, three different sets of items were generated, providing an item pool of 165 items. Before evaluating the efficiency of the GCAT, all items in the final item pool were linked (equated), following a sequential approach. Data were generated using a standard normal for 10,000 virtual individuals ( M = 0 and SD = 1). Using the measure’s 165-item bank, the ability value (θ) for each participant was estimated. maximum Fisher information (MFI) and maximum likelihood estimation with fences (MLEF) were used as item selection and score estimation methods, respectively. For item exposure control, the fade away method (FAM) was preferred. The termination criterion involved a minimum SE ≤ 0.33. The study revealed that the average number of items administered for 10,000 participants was 15. Moreover, the precision level in estimating the participant’s ability score was very high, as demonstrated by the CBIAS, CMAE, and CRMSE). It is concluded that the CAT version of the test is a promising alternative to administering the corresponding full-length measure since it reduces the number of administered items, prevents high rates of item exposure, and provides accurate scores with minimum measurement error.

scholarly journals Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data

Econometrics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 40
Author(s):  
Erhard Reschenhofer ◽  
Manveer K. Mangat
Keyword(s):  
Simulation Study ◽  
High Frequency ◽  
Mean Squared Error ◽  
Estimation Methods ◽  
High Frequency Data ◽  
Frequency Data ◽  
Periodic Patterns ◽  
Typical Sample ◽  
Squared Error ◽  
Proper Interpretation

For typical sample sizes occurring in economic and financial applications, the squared bias of estimators for the memory parameter is small relative to the variance. Smoothing is therefore a suitable way to improve the performance in terms of the mean squared error. However, in an analysis of financial high-frequency data, where the estimates are obtained separately for each day and then combined by averaging, the variance decreases with the sample size but the bias remains fixed. This paper proposes a method of smoothing that does not entail an increase in the bias. This method is based on the simultaneous examination of different partitions of the data. An extensive simulation study is carried out to compare it with conventional estimation methods. In this study, the new method outperforms its unsmoothed competitors with respect to the variance and its smoothed competitors with respect to the bias. Using the results of the simulation study for the proper interpretation of the empirical results obtained from a financial high-frequency dataset, we conclude that significant long-range dependencies are present only in the intraday volatility but not in the intraday returns. Finally, the robustness of these findings against daily and weekly periodic patterns is established.

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