Carole E. Nahum

Multiscale Local Map-Drift-Driven Multilateration SAR Autofocus Using Fast Polar Format Image Synthesis

This new autofocus method is based on multilateration by ranging to small target areas at independent directions on the ground. Range-clipped Doppler low-filtered profiles around target points are used to compute local images using frequency-domain polar format algorithm. Images obtained from adjacent subapertures are registered, and the displacements yield elevation, trajectory, or clock drift (bistatic case) errors. To alleviate the insensitivity of map drift to error fluctuation faster than subaperture duration, the algorithm is reiterated with coarse-to-fine resolution, yielding high to low frequency errors. This allowed true bistatic synthetic aperture radar (SAR) autofocus (without monostatic image), autofocus in circular SAR on remote areas, and, as a side product, our first successful air-to-air inverse SAR high-resolution imaging.

Airborne SAR-Efficient Signal Processing for Very High Resolution

Frequency-domain synthetic aperture radar (SAR) image formation algorithms are of lower computation cost (both in number of elementary operations and in required memory storage) than direct time-domain integration, and do not make the narrowband (monochromatic) assumption. Both advantages are critical to very-high-resolution imaging because a lower complexity yields a drastic computation time decrease as cross-range resolution increases, and the narrowband assumption is more and more a concern as range resolution (hence bandwidth) increases. Though an exact formulation exists (ω- k algorithm) for a perfect linear uniform acquisition trajectory, in a real-life airborne case, the unavoidable trajectory deviation from a straight line needs to be compensated. This motion compensation (MoComp) operation is much more complicated in the case of frequency-domain processing. An efficient technique for this purpose is presented. This method keeps the parallel processing aspect, and has been programmed both for multithread on multicore/symmetrical multiprocessor central processing units (CPUs) and for graphic processor units (GPUs).

Exploitation of C-Band Sentinel-1 Images for High-Resolution Wind Field Retrieval in Coastal Zones (Iroise Coast, France)

Synthetic aperture radar (SAR) is one of the favorite tools for earth observation applications, i.e., oceanography, land use mapping, climate change since this device can offer the data at a high spatial resolution and in most meteorological conditions. This is more significant when the data acquired by the Sentinel-1, a new C-band satellite, are exploited. For high-resolution wind field extraction, two different approaches are proposed. In the scatterometry-based approach, wind direction is first extracted by the local gradient method at different scales, i.e., 1–5-km wind resolutions. It is then applied to the empirical geophysical model functions, i.e., CMOD (C-band), for surface wind speed estimation. The advantage of this approach is to deliver accurate wind speed estimates in the range of 2–25 m/s from different SAR data. However, it requires wind direction as an input parameter. This can lead to errors in wind speed estimation due to uncertain wind directions. Therefore, for comparison, in the second approach, we propose the use of the model without wind direction input proposed by Komarov et al. In general, the obtained wind fields based on two proposed approaches are quite similar, and they have good agreement with in situ measurements from the meteorological stations along the Iroise coast.

Accurate geometrical model for airborne synthetic aperture radar

The accurate knowledge of the geometry of an airborne SAR image is crucial for many applications. Repeat-pass SAR interferometry requires an accurate image matching to the 10th of a pixel, while less demanding applications such as wide area coverage by multi-pass composite or image geocoding tolerate mismatches of pixels. Compared to spaceborne SAR, those accuracy requirements are stronger for airborne SAR sine they provide higher resolution. Moreover, irregularities in aircraft motion due to air turbulence introduce severe geometrical distortions in the images. Unfortunately, these distortions are tightly coupled with the signal processing algorithm used for computing SAR images. We have implemented a geometrical error model for our generic off-line SAR processor which provides an image distortion map. Derivatives with respect to errors in radar parameters and errors in aircraft trajectory measurements (velocity, altitude, oscillations are also provided, thus allowing the efficient estimation of the errors from distortion measurements (tiepoints). The paper is illustrated with some relevant application examples.

Study of inversion EM models for wind speed retrieval from Sentinel-1 data

Sea surface wind speed plays a key parameter in the studies of many oceanic applications, i.e. meteorological forecasting, oil slick observation, ship detection, and wind turbine installation recently. It can be obtained from many available wind sources, i.e. measured data, numeric weather models, etc. However, one of the most well-known ways is the retrieval of wind speed from Synthetic Aperture Radar (SAR) data. For this approach, the studies are based on two principal ways: one uses empirical models and the other is based on electromagnetic calculations. In both indicated approaches, the Geophysical Model Functions (GMFs) are used to describe the dependency of radar scattering from sea surface on surface wind speed and the geometry of observations. By knowing radar scattering and geometric parameters from SAR data, it is possible to invert the GMFs to retrieve wind speed. Then, estimated wind speed by two studied models is compared and evaluated with measured data. Based on the comparisons, the advantages and limits of the studied models are analyzed and discussed.

Application of electromagnetic models for sea near-surface wind speed retrieval from C-band SAR images

Synthetic Aperture Radar (SAR) is one of the favorite sources for sea near-surface wind speed retrieval. For this problem, wind speed is principally estimated based on the empirical (EP) models, namely CMOD functions, which are constructed by the observations from spaceborne microwave scatterometers (ERS-1/2). Little studies have mentioned the use of electromagnetic (EM) models for wind speed estimation, probably due to their complicated descriptions. However, it is reasonable to compare wind speed estimates based on the two approaches, since both of them describe the relation of radar scattering and wind field, directly for EP models and via wave surface roughness for EM models. Based on the comparisons, some ideas are proposed to improve the performance of EP and EM models.

Comparison of empirical and electromagnetic geophysical model function for near-surface wind speed retrieval

Despite based on different approaches and objectives, it is reasonable to compare near-surface wind speed estimated by the empirical (EP) and electromagnetic (EP) geophysical model function (GMF), since both of them describe the relation between radar scattering and wind vector (directly for EP GMF and via surface roughness spectrum for EM GMF). In general, the EP and EM models give quite similar normalized radar cross section (NRCS) for radar incidence angle below 40°. Consequently, wind speed estimated by the EP and EM GMF is very close. However, for incidence angles above 40°, the EM models show poor performance of wind speed estimation.

Pirical approach for C-band VV-polarization wind vector retrieval from Sentinel-1 images

Based on an empirical model without wind direction input for the retrieval of C-band HH-polarization wind speed, we propose a modified model for wind speed estimation in VV-polarization. The obtained wind speed is then applied for the CMOD5.N to estimate wind directions. The comparisons with the scatterometry-based approach demonstrate that the estimated wind speed by the proposed model is closer to in situ measurements than that obtained with the CMOD5.N. Likewise, the extracted wind directions from the CMOD5.N are more accurate that those obtained with the local gradient method.

Overview of surface wind speed retrieval from C-band SAR images: Empirical and electromagnetic approaches

In spite of the difference in description, it is reasonable to compare sea surface wind speed estimates based on empirical (EP) and electromagnetic (EM) approaches, since both of them describe the relation between radar backscattering and wind parameters, directly for EP models and via sea surface roughness spectrum for EM models. For EP approach, two methods are presented for wind speed estimation: scatterometry and model without wind direction input. For EM approach, the approximation models, i.e. small-slope approximation (SSA) and resonant curvature approximation (RCA), are presented since they can calculate radar backscattering close to that given by the EP models. The comparison between EP and EM models demonstrate that estimated wind speeds by two approaches are quite similar, especially for radar incidence angles below 40°.

Sensitivity of sea wind direction retrieval from Sentinel-1 data with regard to spatial resolution and speckle noise

Wind direction is a crucial parameter in many inversion models to estimate wind speed from Synthetic Aperture Radar (SAR) data. Compared to the other available wind sources, i.e. measured data, numeric weather data, etc., the retrieval of wind directions from SAR data is more widely used, since it can give wind directions at different scales. Nevertheless, there are not a lot of studies which report about the sensitivity of wind direction retrieval, particularly with regard to the spatial resolution (or acquisition mode) of SAR images, speckle noise, and wind regimes. In order to investigate this issue, the Local Gradient method is selected to retrieve wind directions from the Sentinel-1 images at different scales, since it can give high wind resolution cells. Then, the impact of speckle noise and wind regimes on retrieved wind directions is assessed.