On the use of the l(2)-norm for texture analysis of polarimetric SAR data

In this paper, the use of the l2-norm, or Span, of the scattering vectors is suggested for texture analysis of polarimetric synthetic aperture radar (SAR) data, with the benefits that we need neither an analysis of the polarimetric channels separately nor a filtering of the data to analyze the statistics. Based on the product model, the distribution of the l2-norm is studied. Closed expressions of the probability density functions under the assumptions of several texture distributions are provided. To utilize the statistical properties of the l2-norm, quantities including normalized moments and log-cumulants are derived, along with corresponding estimators and estimation variances. Results on both simulated and real SAR data show that the use of statistics based on the l2-norm brings advantages in several aspects with respect to the normalized intensity moments and matrix variate log-cumulants.Peer ReviewedPostprint (published version

Accurate Despeckling and Estimation of Polarimetric Features by Means of a Spatial Decorrelation of the Noise in Complex PolSAR Data

In this work, we extended a procedure for the spatial decorrelation of fully-developed speckle, originally developed for single-polarization SAR data, to fully-polarimetric SAR data. The spatial correlation of the noise depends on the tapering window in the Fourier domain used by the SAR processor to avoid defocusing of targets caused by Gibbs effects. Since each polarimetric channel is focused independently of the others, the noise-whitening procedure can be performed applying the decorrelation stage to each channel separately. Equivalently, the noise-whitening stage is applied to each element of the scattering matrix before any multilooking operation, either coherent or not, is performed. In order to evaluate the impact of a spatial decorrelation of the noise on the performance of polarimetric despeckling filters, we make use of simulated PolSAR data, having user-defined polarimetric features. We optionally introduce a spatial correlation of the noise in the simulated complex data by means of a 2D separable Hamming window in the Fourier domain. Then, we remove such a correlation by using the whitening procedure and compare the accuracy of both despeckling and polarimetric features estimation for the three following cases: uncorrelated, correlated, and decorrelated images. Simulation results showed a steady improvement of performance scores, most notably the equivalent number of looks (ENL), which increased after decorrelation and closely attained the value of the uncorrelated case. Besides ENL, the benefits of the noise decorrelation hold also for polarimetric features, whose estimation accuracy is diminished by the correlation. Also, the trends of simulations were confirmed by qualitative results of experiments carried out on a true Radarsat-2 image

Offshore Metallic Platforms Observation Using Dual-Polarimetric TS-X/TD-X Satellite Imagery: A Case Study in the Gulf of Mexico

Satellite-based synthetic aperture radar (SAR) has been proven to be an effective tool for ship monitoring. Offshore platforms monitoring is a key topic for both safety and security of the maritime domain. However, the scientific literature oriented to the observation of offshore platforms using SAR imagery is very limited. This study is mostly focused on the analysis and understanding of the multipolarization behavior of platforms’ backscattering using dual-polarization X-band SAR imagery. This study is motivated by the fact that under low incidence angle and moderate wind conditions, copolarized channels may fail in detecting offshore platforms even when fine-resolution imagery is considered. This behavior has been observed on both medium- and high-resolution TerraSAR-X/TanDEM-X SAR imagery, despite the fact that platforms consist of large metallic structures. Hence, a simple multipolarization model is proposed to analyze the platform backscattering. Model predictions are verified on TerraSAR-X/TanDEM-X SAR imagery, showing that for acquisitions under low incidence angle, the platforms result in a reduced copolarized backscattered intensity even when fine resolution imagery is considered. Finally, several solutions to tackle this issue are proposed with concluding remark that the performance of offshore observation

Polarimetric Synthetic Aperture Radar (SAR) Application for Geological Mapping and Resource Exploration in the Canadian Arctic

The role of remote sensing in geological mapping has been rapidly growing by providing predictive maps in advance of field surveys. Remote predictive maps with broad spatial coverage have been produced for northern Canada and the Canadian Arctic which are typically very difficult to access. Multi and hyperspectral airborne and spaceborne sensors are widely used for geological mapping as spectral characteristics are able to constrain the minerals and rocks that are present in a target region. Rock surfaces in the Canadian Arctic are altered by extensive glacial activity and freeze-thaw weathering, and form different surface roughnesses depending on rock type. Different physical surface properties, such as surface roughness and soil moisture, can be revealed by distinct radar backscattering signatures at different polarizations. This thesis aims to provide a multidisciplinary approach for remote predictive mapping that integrates the lithological and physical surface properties of target rocks. This work investigates the physical surface properties of geological units in the Tunnunik and Haughton impact structures in the Canadian Arctic characterized by polarimetric synthetic aperture radar (SAR). It relates the radar scattering mechanisms of target surfaces to their lithological compositions from multispectral analysis for remote predictive geological mapping in the Canadian Arctic. This work quantitatively estimates the surface roughness relative to the transmitted radar wavelength and volumetric soil moisture by radar scattering model inversion. The SAR polarization signatures of different geological units were also characterized, which showed a significant correlation with their surface roughness. This work presents a modified radar scattering model for weathered rock surfaces. More broadly, it presents an integrative remote predictive mapping algorithm by combining multispectral and polarimetric SAR parameters

Dynamical Approach for Real-Time Monitoring of Agricultural Crops

In this paper, a novel approach for exploiting multitemporal remote sensing data focused on real-time monitoring of agricultural crops is presented. The methodology is defined in a dynamical system context using state-space techniques, which enables the possibility of merging past temporal information with an update for each new acquisition. The dynamic system context allows us to exploit classical tools in this domain to perform the estimation of relevant variables. A general methodology is proposed, and a particular instance is defined in this study based on polarimetric radar data to track the phenological stages of a set of crops. A model generation from empirical data through principal component analysis is presented, and an extended Kalman filter is adapted to perform phenological stage estimation. Results employing quad-pol Radarsat-2 data over three different cereals are analyzed. The potential of this methodology to retrieve vegetation variables in real time is shown.This work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Project TEC2011-28201-C02-02 and in part by the Generalitat Valenciana under Project ACOMP/2014/136

Generative feature extraction from sentinel 1 and 2 data for prediction of forest aboveground biomass in the Italian Alps

—Aboveground biomass (AGB) is an important forest attribute directly linked to the forest carbon pool. The use of satellite remote sensing (RS) data has increased for AGB prediction due to their large footprint and low-cost availability. However, they have been limited due to saturation effect that leads to low prediction precision. In this article, we propose an innovative and dynamic architecture based on generative neural network that extracts target oriented generative features for predicting forest AGB using satellite RS data. These features are more resilient to mixed forest types and geographical conditions as compared to the traditional features and models. The effectiveness of the proposed features was assessed by experiments performed using multispectral, synthetic aperture radar, and combined dual-source datasets. The proposed model achieved best performance in terms of precision, model agreement, and overfitting as compared to the other conventional models for all analyzed datasets. The t-distributed stochastic neighbor embedding scatterplots of the generative features clearly show one dimension of the feature space associated with the target AGB. Feature importance analysis indicated that the produced generative features were more significant than the conventional analytical features. Also, the model provided a robust framework for homogeneous fusion of multisensor features from satellite RS data for predicting AG

Disaster debris estimation using high-resolution polarimetric stereo-SAR

AbstractThis paper addresses the problem of debris estimation which is one of the most important initial challenges in the wake of a disaster like the Great East Japan Earthquake and Tsunami. Reasonable estimates of the debris have to be made available to decision makers as quickly as possible. Current approaches to obtain this information are far from being optimal as they usually rely on manual interpretation of optical imagery. We have developed a novel approach for the estimation of tsunami debris pile heights and volumes for improved emergency response. The method is based on a stereo-synthetic aperture radar (stereo-SAR) approach for very high-resolution polarimetric SAR. An advanced gradient-based optical-flow estimation technique is applied for optimal image coregistration of the low-coherence non-interferometric data resulting from the illumination from opposite directions and in different polarizations. By applying model based decomposition of the coherency matrix, only the odd bounce scattering contributions are used to optimize echo time computation. The method exclusively considers the relative height differences from the top of the piles to their base to achieve a very fine resolution in height estimation. To define the base, a reference point on non-debris-covered ground surface is located adjacent to the debris pile targets by exploiting the polarimetric scattering information. The proposed technique is validated using in situ data of real tsunami debris taken on a temporary debris management site in the tsunami affected area near Sendai city, Japan. The estimated height error is smaller than 0.6m RMSE. The good quality of derived pile heights allows for a voxel-based estimation of debris volumes with a RMSE of 1099m3. Advantages of the proposed method are fast computation time, and robust height and volume estimation of debris piles without the need for pre-event data or auxiliary information like DEM, topographic maps or GCPs