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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.
Alexandre Corazza; Ali Khenchaf; Fabrice Comblet. Assessment of Wind Direction Estimation Methods from SAR Images. Remote Sensing 2020, 12, 3631 .
AMA StyleAlexandre Corazza, Ali Khenchaf, Fabrice Comblet. Assessment of Wind Direction Estimation Methods from SAR Images. Remote Sensing. 2020; 12 (21):3631.
Chicago/Turabian StyleAlexandre Corazza; Ali Khenchaf; Fabrice Comblet. 2020. "Assessment of Wind Direction Estimation Methods from SAR Images." Remote Sensing 12, no. 21: 3631.
In the recent years, multi-constellation and multi-frequency have improved the positioning precision in GNSS applications and significantly expanded the range of applications to new areas and services. However, the use of multiple signals presents advantages as well as disadvantages, since they may contain poor quality signals that negatively impact the position precision. The objective of this study is to improve the Single Point Positioning (SPP) accuracy using multi-GNSS data fusion. We propose the use of robust-Extended Kalman Filter (referred to as robust-EKF hereafter) to eliminate outliers. The robust-EKF used in the present work combines the Extended Kalman Filter with the Iterative ReWeighted Least Squares (IRWLS) and the Receiver Autonomous Integrity Monitoring (RAIM). The weight matrix in IRWLS is defined by the MM Estimation method which is a robust statistics approach for more efficient statistical data analysis with high breaking point. The RAIM algorithm is used to check the accuracy of the protection zone of the user. We apply the robust-EKF method along with the robust combination of GPS, Galileo and GLONASS data from ABMF base station, which significantly improves the position accuracy by about 84% compared to the non-robust data combination. ABMF station is a GNSS reception station managed by Météo-France in Guadeloupe . Thereafter, ABMF will refer to the acronym used to designate this station. Although robust-EKF demonstrates improvement in the position accuracy, its outputs might contain errors that are difficult to estimate. Therefore, an algorithm that can predetermine the error produced by robust-EKF is needed. For this purpose, the long short-term memory (LSTM) method is proposed as an adapted Deep Learning-Based approach. In this paper, LSTM is considered as a de-noising filter and the new method is proposed as a hybrid combination of robust-EKF and LSTM which is denoted rEKF-LSTM. The position precision greatly improves by about 95% compared to the non-robust combination of data from ABMF base station. In order to assess the rEKF-LSTM method, data from other base stations are tested. The position precision is enhanced by about 87%, 77% and 93% using the rEKF-LSTM compared to the non-robust combination of data from three other base stations AJAC, GRAC and LMMF in France, respectively.
Truong-Ngoc Tan; Ali Khenchaf; Fabrice Comblet; Pierre Franck; Jean-Marc Champeyroux; Olivier Reichert. Robust-Extended Kalman Filter and Long Short-Term Memory Combination to Enhance the Quality of Single Point Positioning. Applied Sciences 2020, 10, 4335 .
AMA StyleTruong-Ngoc Tan, Ali Khenchaf, Fabrice Comblet, Pierre Franck, Jean-Marc Champeyroux, Olivier Reichert. Robust-Extended Kalman Filter and Long Short-Term Memory Combination to Enhance the Quality of Single Point Positioning. Applied Sciences. 2020; 10 (12):4335.
Chicago/Turabian StyleTruong-Ngoc Tan; Ali Khenchaf; Fabrice Comblet; Pierre Franck; Jean-Marc Champeyroux; Olivier Reichert. 2020. "Robust-Extended Kalman Filter and Long Short-Term Memory Combination to Enhance the Quality of Single Point Positioning." Applied Sciences 10, no. 12: 4335.
Surface wind speed estimation from synthetic aperture radar (SAR) data is principally based on empirical (EP) approaches, e.g., CMOD functions. However, it is necessary and significant to compare radar backscattering modeling based on EP and electromagnetic (EM) approaches for enhancing the understanding of the physical processes between radar signal and sea surface, which is important for the design of radar sensors (e.g., cyclone global navigation satellite system). Indeed, through comparisons, it is worth noticing that the scattering of wave breaking is not taken into account in the physical modeling of radar backscattering. Surface wind speed is selected here as a reference parameter for investigating the difference between EP and EM models, due to its important role in radar backscattering modeling. In addition, wind speed estimates can be easily compared to in situ measurements. For EP approach, CMOD5.N and Komarov's model are selected for wind speed estimation from Sentinel-1 images. The CMOD5.N can offer wind speed estimates up to 25-35 m/s, while wind speed estimation based on Komarov's model does not require wind direction input. For EM approach, the asymptotic models, i.e., composite two-scale model, small-slope approximation (SSA), and resonant curvature approximation (RCA), are investigated for wind speed retrieval. They are studied with two models of surface roughness spectrum: semi-EP spectrum and EP model. In general, normalized radar cross section (NRCS) calculated by CMOD5.N and SSA/RCA is quite similar for incidence angles below 40° in vertical polarized and below 30° in horizontal polarized. For larger ones, significant NRCS deviations between two approaches are demonstrated, due to the lack of wave breaking scattering in EM models. As a result, wind speed estimates by CMOD5.N and SSA/RCA are very close for low and moderate incidence angles, while SSA-/RCA-based wind speeds are overestimated for larger ones.
Tran Vu La; Ali Khenchaf; Fabrice Comblet; Carole Nahum. Assessment of Wind Speed Estimation From C-Band Sentinel-1 Images Using Empirical and Electromagnetic Models. IEEE Transactions on Geoscience and Remote Sensing 2018, 56, 4075 -4087.
AMA StyleTran Vu La, Ali Khenchaf, Fabrice Comblet, Carole Nahum. Assessment of Wind Speed Estimation From C-Band Sentinel-1 Images Using Empirical and Electromagnetic Models. IEEE Transactions on Geoscience and Remote Sensing. 2018; 56 (7):4075-4087.
Chicago/Turabian StyleTran Vu La; Ali Khenchaf; Fabrice Comblet; Carole Nahum. 2018. "Assessment of Wind Speed Estimation From C-Band Sentinel-1 Images Using Empirical and Electromagnetic Models." IEEE Transactions on Geoscience and Remote Sensing 56, no. 7: 4075-4087.
Among the different available wind sources, i.e. in situ measurements, numeric weather models, the retrieval of wind speed from Synthetic Aperture Radar (SAR) data is one of the most widely used methods, since it can give high wind resolution cells. For this purpose, one can find two principal approaches: via electromagnetic (EM) models and empirical (EP) models. In both approaches, the Geophysical Model Functions (GMFs) are used to describe the relation of radar scattering, wind speed, and the geometry of observations. By knowing radar scattering and geometric parameters, it is possible to invert the GMFs to retrieve wind speed. It is very interesting to compare wind speed estimated by the EM models, general descriptions of radar scattering from sea surface, to the one estimated by the EP models, specific descriptions for the inverse problem. Based on the comparisons, some ideas are proposed to improve the performance of the EM models for wind speed retrieval.
Tran Vu La; Ali Khenchaf; Fabrice Comblet; Carole Nahum; Helmi Ghanmi. Exploitation of Electromagnetic Models for Sea Wind Speed Estimation from C-Band Sentinel-1 Images. Journal of Electromagnetic Analysis and Applications 2016, 08, 42 -55.
AMA StyleTran Vu La, Ali Khenchaf, Fabrice Comblet, Carole Nahum, Helmi Ghanmi. Exploitation of Electromagnetic Models for Sea Wind Speed Estimation from C-Band Sentinel-1 Images. Journal of Electromagnetic Analysis and Applications. 2016; 08 (03):42-55.
Chicago/Turabian StyleTran Vu La; Ali Khenchaf; Fabrice Comblet; Carole Nahum; Helmi Ghanmi. 2016. "Exploitation of Electromagnetic Models for Sea Wind Speed Estimation from C-Band Sentinel-1 Images." Journal of Electromagnetic Analysis and Applications 08, no. 03: 42-55.
Helmi Ghanmi; Ali Khenchaf; Fabrice Comblet. A new method for reliable detection of polluted sea surfaces. SPIE Professional 2015, 1 .
AMA StyleHelmi Ghanmi, Ali Khenchaf, Fabrice Comblet. A new method for reliable detection of polluted sea surfaces. SPIE Professional. 2015; ():1.
Chicago/Turabian StyleHelmi Ghanmi; Ali Khenchaf; Fabrice Comblet. 2015. "A new method for reliable detection of polluted sea surfaces." SPIE Professional , no. : 1.
The aim of this work is to study the impacts of the oil spills on the electromagnetic scattering of the ocean surfaces in bistatic and monostatic configurations. Therefore, in this paper, we will study the influence of the pollutants (oil spills) on the physical and geometrical properties of sea surface. In recent literature, the study of the electromagnetic scattering from contaminated sea surface (sea surface covered by oil spill) was limited in monostatic case. In this paper, we will study this effect in bistatic configuration, which is interested in presence of pollution in sea surface. Indeed, we will start the numerical analysis of the bistatic scattering coefficients of a clean sea surface. Then, we will study the electromagnetic signature from sea surface covered by oil spills in bistatic case using the numerical Forward-Backward Method (FBM). The obtained numerical simulation of bistatic scattering coefficients of clean and contaminated sea surface is studied as a function of various parameters (frequency, incident angle, sea state, type of pollutant…). And the obtained results are also compared with those published in the literature, including those using asymptotic methods.
Helmi Ghanmi; Ali Khenchaf; Fabrice Comblet. Numerical Modeling of Electromagnetic Scattering from Sea Surface Covered by Oil. Journal of Electromagnetic Analysis and Applications 2014, 06, 15 -24.
AMA StyleHelmi Ghanmi, Ali Khenchaf, Fabrice Comblet. Numerical Modeling of Electromagnetic Scattering from Sea Surface Covered by Oil. Journal of Electromagnetic Analysis and Applications. 2014; 06 (01):15-24.
Chicago/Turabian StyleHelmi Ghanmi; Ali Khenchaf; Fabrice Comblet. 2014. "Numerical Modeling of Electromagnetic Scattering from Sea Surface Covered by Oil." Journal of Electromagnetic Analysis and Applications 06, no. 01: 15-24.