In this paper we first highlight a new approach to the analysis of compact polarimetric imaging radar data, based on decomposition theory. We then use a time series of Radarsat-2 quadpol data acquisitions collected over the autumn and winter of 2011/12 for a calibrated forest test site near Hinton in Alberta, Canada, which contains a mixed forest, seminatural vegetation and mountainous terrain environment. This data is collected in the new wide-swath quadpol mode FQW of Radarsat-2, which matches the wider range swath capability of any future compact mode. This data is first used to simulate compact mode using circular polarization transmit and dual linear receive and the co-registered multitemporal stack then employed for a rule-based classifier to determine land-use types compared against a reference landuse map. We compare the information obtained from compact against a standard dualpol linear transmit and dual linear receive, as proposed for example in the ESA Sentinel missions, to confirm the utility of using circular polarization for enhanced land-use products at C-band.
This paper deals with the depolarization properties of rough surface back-scattering at visible and infra-red wavelengths. It is well known that rough surfaces depolarize incident light. In this paper we analyze the structure of surface depolarization using a coherency matrix approach and show that quantitative estimates of roughness may be obtained from a single Mueller matrix parameter, the scattering anisotropy A. Multi-spectral backscatter Mueller matrix data has been obtained for a set of controlled laboratory measurements on roughened Aluminum surfaces. The surfaces were manufactured using a bead-blasting process and the surface statistics have been carefully measured. We then applied a technique first developed for applications in radar scattering, which involves transformation of the Mueller matrix into a complex hermitian coherency matrix with subsequent eigenanalysis. These eigenvalues are then used to characterize the depolarizing properties of the surface. We show how important surface information is contained in the eigenvalue variation with angle of incidence and conclude as to the possibility for quantitative surface roughness measurements using this technique.
In this paper we consider the relationship between surface roughness in scattering polarimetry and the eigenvalues of the 4 X 4 scattering coherency matrix. These eigenvalues have already been employed in Mueller matrix filtering and particle scattering studies, but here we show that they have a physical significance in terms of scattering amplitudes and that their ratios represent generalized measures of polarimetric coherence. One such ratio, the entropy H, has been introduced in earlier publications, but here we show that a new measure, the anisotropy A, can also be used for surface roughness studies. This parameter is closely linked to the circular polarization coherence but generalizes the concept to account for any residual correlations that may be present in the surface scattering. Increasing surface roughness causes depolarization and hence increasing scattering entropy. However, the distribution of depolarized energy amongst the smaller eigenvalues contains important information about the structure of the surface. Here we suggest how this information may be interpreted and demonstrate the theory by applying it to experimental data collected for laboratory manufactured rough surfaces with known statistical properties.
Classification of Earth terrain components within a full polarimetric SAR image is one of the most important applications of Radar Polarimetry in Remote Sensing. Unsupervised classification procedure, based around neural networks with competitive architecture, is applied to the full polarimetric SAR images of San Francisco Bay from the NASA/JPL AIRSAR data base (1988) for segmentation and clustering of different Earth terrain components. The linear feature vector used during the classification procedure is defined from a new scheme for parameterizing polarimetric scattering problems, which has application in the quantitative analysis of polarimetric SAR data. The method relies on an eigenvalue analysis of the coherency matrix and employs a 3-level Bernoulli statistical model to generate estimates of the average target scattering matrix parameters from the data.
This contribution is concerned with coherent polarimetric bistatic scattering of plane electromagnetic waves from deterministic radar targets. The Huynen fork descriptor for the monostatic backscatter case describing the relations between optimal characteristic target polarizations on the Poincare sphere is extended to the bistatic scattering case by introducing a double Huynen fork for transmission and reception. The analysis relies on the singular value decomposition theorem.
In this paper we show how the entropy-alpha target decomposition scheme may be used for parametric inversion studies on particle clouds. The decomposition is first presented in detail and then applied to a 2-parameter model for backscatter from a random cloud of small anisotropic particles. The two parameters are the mean particle shape and the mean orientation angle of the cloud. An inversion algorithm is presented and applied to broad band polarimetric Radar data from the European Microwave Scattering Laboratory at JRC Ispra.
KEYWORDS: L band, Interferometry, Scattering, Polarimetry, Vegetation, Synthetic aperture radar, Buildings, Agriculture, Signal to noise ratio, Data acquisition
The investigation presented in this paper demonstrates the potential of the combination of polarimetric and interferometric classification techniques for the extraction of map relevant features from space borne SAR data. In the first part we discuss a polarimetric classification technique based on Cloude's decomposition theorem. Afterwards we demonstrate the abilities of interferometric classification. The complementarity of the polarimetric and interferometric coherence based classification approaches can be used to resolve ambiguities that remain if one method is applied alone. The improvements results from their combination are available for an automatic classification and extraction of cartographic relevant features from space borne SAR data.
In this paper we outline a general formulation of vector wave interferometry and then use this formulation to solve the optimization problem for interferometric coherence. We show that this problem can be reduced to a singular value decomposition of a non-symmetric complex matrix. We then develop a stochastic scattering model for an elevated forest canopy and use it to demonstrate application of the optimization scheme.
This contribution is concerned with the theory of optical and radar polarimetry dealing with forward (transmission) and backscatter polarimetric scattering. Characteristic similarities and differences between these topics are pointed out and typical polarimetric invariants are identified.
In this paper we consider the physical interpretation of eigenvalues and eigenvectors in the coherency matrix formulation of optical polarimetry. The coherency matrix formulation is relatively new in optics and so is first developed and compared with the classical Mueller matrix formulation. It is shown that by employing a special kind of averaging based on a Bernoulli multi-symbol model using the eigenvector decomposition, physical parameters of the medium may be related to matrix observables. To illustrate this approach, the problem of scattering by a cloud of anisotropic particles with variable stochastic properties is used. It is shown in particular that a 2D plane, the entropy/alpha plane, is important for the representation of scattering data. The technique has potential application in data inversion studies in optical scattering polarimetry.
We consider the application of the general theory of unitary matrices to problems of wave scattering involving polarized waves. Haying outlined useful parameterizations of the low dimensional groups associated with these unitary matrices, we develop a general processing strategy, which we suggest has application in the extraction of physical information from a range of scattering matrices in optics. Examples are presented of applying the unitary matrix structure to problems of single and multiple scattering from a cloud of random particles. The techniques are best suited to characterization of depolarizing systems, where the scattered waves undergo a change of degree as well as polarization state. The degree of disorder of the system is then quantified by a scalar, the polarimetric entropy, defined from the eigenvalues of a scattering matrix that ranges from 0 for systems with zero scattering to 1 for perfect depolarizers. Further, we show that the unitary matrix parameterization can be used to extract important system information from the eigenvectors of this matrix.
In this paper we consider application of the general theory of unitary matrices to the problem of wave propagation and scattering involving polarized waves. Having outlined useful parameterizations of these low dimensional matrix groups, we then develop a general processing strategy which we suggest is useful for the extraction of physical information from a range of scattering and propagation matrices in optics and radar. Examples are presented of application of the unitary matrix structure to the problems of absolute phase definition and random wave scattering.
ultra wide band (UWB) time domain radar signals and presents experimental measurements used to illustrate the advantages and disadvantages of such methods. The techniques are developed in three stages; early time processing based on time domain inverse scattering identities for the design of matched filter detectors, radar cross section modelling using finite difference time domain techniques for investigating the effects of change of target geometry and incident pulse shape and finally, the use of late time information on target resonance and damping for the identification of important target features. Two targets are considered in detail, backscatter from a metallic sphere and scattering from a rectangular box with variable aperture sizes.
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