Sensitivity of Main Polarimetric Parameters of Multifrequency Polarimetric SAR Data to Soil Moisture and Surface Roughness Over Bare Agricultural Soils

International audienceThe potential of polarimetric synthetic aperture radar data for the soil surface characterization of bare agricultural soils was investigated by using air- and spaceborne data acquired by Radar Aéroporté Multi-Spectral d'Etude des Signatures (RAMSES), Système Expérimental de Télédétection Hyperfréquence Imageur (SETHI), and RADARSAT-2 sensors over several study sites in France. Fully polarimetric data at ultrahigh frequency, X-, C-, L-, and P-bands were compared. The results show that the main polarimetric parameters studied (entropy, α angle, and anisotropy) are not very sensitive to the variation of the soil surface parameters. Low correlations are observed between the polarimetric and soil parameters (moisture content and surface roughness). Thus, the polarimetric parameters are not very relevant to the characterization of the soil surface over bare agricultural areas

Heterogeneous Clutter Model for High-Resolution Polarimetric SAR Data Processing

International audienceThis paper presents a new estimation scheme for optimally deriving clutter parameters with high resolution POLSAR data. The heterogeneous clutter in POLSAR data is described by the Spherically Invariant Random Vectors model. Three parameters are introduced for the high resolution POLSAR data clutter: the span, the normalized texture and the speckle normalized covariance matrix. The asymptotic distribution of the novel span estimator is investigated. A novel heterogeneity test for the POLSAR clutter is also discussed. The proposed method is tested with airborne POLSAR images provided by the ONERA RAMSES system

SETHI / RAMSES-NG: New performances of the flexible multi-spectral airborne remote sensing research platform

International audienceSETHI is an airborne SAR/GMTI system developed by the French Aerospace Lab. ONERA, and integrating various sensors. In 2016 ONERA invested in upgrade and improvement of all SETHI components. The microwave ones cover from VHF-UHF to X Band, full polarimetric and very high resolution, along track and cross track interferometry and very high precision multi-baseline capacity for interferometry and tomography applications. The optronic sensors offer very high spatial resolution visible images and fine spectral scene analysis in VNIR and SWIR bands. This paper presents the upgrade and new performances of this flexible platform and the qualification campaign results with various sensor configurations

Optimal Parameter Estimation in Heterogeneous Clutter for High Resolution Polarimetric SAR Data

International audienceThis letter presents a new estimation scheme for optimally deriving clutter parameters with high-resolution polarimetric synthetic aperture radar (POLSAR) data. The heterogeneous clutter in POLSAR data is described by the spherically invariant random vector model. Three parameters are introduced for the high-resolution POLSAR data clutter: the span, the normalized texture, and the speckle normalized covariance matrix. The asymptotic distribution of the novel span estimator is investigated. A novel heterogeneity test for the POLSAR clutter is also discussed. The proposed method is tested with airborne POLSAR images provided by the Office National d'Études et de Recherches Aerospatiales Radar Aéroporté Multi-spectral d'Etude des Signatures system

CFAR Hierarchical Clustering of Polarimetric SAR Data

International audienceRecently, a general approach for high-resolution polarimetric SAR (POLSAR) data classification in heterogeneous clutter was presented, based on a statistical test of equality of covariance matrices. Here, we extend that approach by taking advantage of the Constant False Alarm Ratio (CFAR) property of the statistical test in order to improve the clustering process. We show that the CFAR property can be used in the hierarchical segmentation of the POLSAR data images to automatically detect the number of clusters. The proposed method will be applied on a high-resolution polarimetric data set acquired by the ONERA RAMSES system

Imaging of snow/ice subsurface features from airborne SAR at UHF, L and X band. The ONERA SAR campaign in South Greenland

International audienceAn acquisition campaign of the ONERA airborne synthetic aperture radar system RAMSES have been set up on the Greenland ice-shelf to assess the detectability of buried objects and subsurface feature through dry snow/ice cover. Three band were investigated UHF band, L band and X band, forecasting a better penetration at lower band, but an higher radar cross section for a given object/feature at higher band, thus a trade off for the optimal frequency band to use that was not clear prior to experiment. Three sites were imaged, one in the accumulation zone North of the polar circle (East of Kangerlussuaq airport), one in the accumulation zone in a warmer southern area (East of Narsassuaq airport) and one in the intermediate zone between accumulation and ablation zones (plus a calibration zone and a front glacier close to Kangerlussuaq). Processing of the data revealed that both UHF and X band are indeed providing complementing results (deeper bigger feature versus shallow smaller features), but intermediate L band combining both drawbacks of higher attenuation and lower features RCS did not seem to yield any further information

Hierarchical Segmentation of Polarimetric SAR Images Using Heterogeneous Clutter Models

International audienceIn this paper, heterogeneous clutter models are used to describe polarimetric synthetic aperture radar (PolSAR) data. The KummerU distribution is introduced to model the PolSAR clutter. Then, a detailed analysis is carried out to evaluate the potential of this new multivariate distribution. It is implemented in a hierarchical maximum likelihood segmentation algorithm. The segmentation results are shown on both synthetic and high-resolution PolSAR data at the X- and L-bands. Finally, some methods are examined to determine automatically the "optimal" number of segments in the final partition

H/α Unsupervised Classification for Highly Textured Polinsar Images using Information Geometry of Covariance Matrices

International audienceWe discuss in the paper the use of the Riemannian mean given by the differential geometric tools. This geometric mean is used in this paper for computing the class centers in the polarimetric H/α unsupervised classification process. We show that the class centers remain more stable during the iteration process, leading to a different interpretation of the H/α /A classification. This technique can be applied both on classical Sample Covariance Matrix and on Fixed Point covariance matrices. Used jointly with the Fixed Point covariance matrix estimate, this technique can give more robust results when dealing with high resolution and highly textured polarimetric SAR images classification

Statistical Classification for Heterogeneous Polarimetric SAR Images

International audienceThis paper presents a general approach for high-resolution polarimetric SAR data classification in heterogeneous clutter, based on a statistical test of equality of covariance matrices. The Spherically Invariant Random Vector (SIRV) model is used to describe the clutter. Several distance measures, including classical ones used in standard classification methods, can be derived from the general test. The new approach provide a threshold over which pixels are rejected from the image, meaning they are not sufficiently "close" from any existing class. A distance measure using this general approach is derived and tested on a high-resolution polarimetric data set acquired by the ONERA RAMSES system. It is compared to the results of the classical decomposition and Wishart classifier under Gaussian and SIRV assumption. Results show that the new approach rejects all pixels from heterogeneous parts of the scene and classifies its Gaussian parts

BIOSAR 2010 - A SAR campaign in support to the BIOMASS mission

The ESA funded campaign BioSAR 2010 was carried out at the forestry test site Remningstorp in southern Sweden, in support to the BIOMASS satellite mission under study. Fully polarimetric SAR data were successfully acquired at L- and P-band using ONERA's multi-frequency system SETHI. In addition with other data types gathered, e.g. LiDAR and in-situ measurements, the compiled data set will be used for analyses and comparisons with biomass estimation results obtained at the same test site in the campaign BioSAR 2007, in which DLR's E-SAR made the SAR imaging. Detection of forest changes, robustness of biomass retrieval algorithms and long-term P-band coherence will be in focus as well as cross-validations between the two SAR sensors