Coherent Target Decomposition Research Articles

Ocean Clutter Characterization Based on PolSAR Data and Second-Order Statistics of Elementary Scatterers

New features are proposed for sea clutter characterization when PolSAR data are employed. Cameron coherent target decomposition is applied to characterize each SAR pixel by means of the 8 basic elementary scatterers obtained by this decomposition. Since the examined SAR pixel does not match exactly to a specific ideal elementary scatterer, the closest scatterer is assigned to this pixel along with its distance (closeness) from the ideal one. The features proposed are (a) the percentage of each elementary scatterer in the investigated region; (b) the distribution of the closeness of each declared elementary scattering mechanism to its ideal counterpart; (c) the co-occurrence of the same scattering mechanism, taking into consideration its distance from the ideal one; and (d) The co-occurrence of the different scattering mechanisms in pairs, taking again into consideration their distance from the ideal ones. Simultaneously, the skewness and the kurtosis and their 2D versions of the previously mentioned probabilities are employed to further improve ocean clutter characterization. The above features correspond to the sea state condition in each separate region, i.e., the wave height and the wind speed. A clear correspondence between the proposed features and the sea state conditions is established. Data were available from RADARSAT-2 and ALOS-1 PALSAR systems for four different ocean regions on Earth.

Potential performance of polarimetric reference function of SAR data processing by coherent target decomposition

Significant employment of polarimetric synthetic aperture radar (SAR) is the classification of the Earth’s surface, which includes several processing algorithms, labelling, and representation options. This paper investigated the potential performance of the polarimetric reference function in SAR data processing to apply in coherent decomposition. The proposed methodology employed the simulated radar cross section of dihedral and trihedral corner reflector under a SAR geometry using physical optics (PO) and geometrical optics (GO) to produce the polarimetric reference function for azimuth compression in the processing of SAR data. The Pauli and the Krogager decompositions processed the compressed SAR data to investigate the effect and performance of the polarimetric reference function. The colour clustering results based on k-mean clustering algorithm could deliver more related scattering characteristic. The image pixel classification was strongly associated with the polarimetric reference function in processing step. These findings indicated that the obtained decomposition and clustering results could provide a better performance in visual impact and quantitative evaluation according to the decomposition term, as well as its physical interpretation.

Fully Polarimetric Land Cover Classification Based on Markov Chains

A novel land cover classification procedure is presented utilizing the information content of fully polarimetric SAR images. The Cameron coherent target decomposition (CTD) is employed to characterize land cover pixel by pixel. Cameron’s CTD is employed since it provides a complete set of elementary scattering mechanisms to describe the physical properties of the scatterer. The novelty of the proposed land classification approach lies on the fact that the features used for classification are not the types of the elementary scatterers themselves, but the way these types of scatterers alternate from pixel to pixel on the SAR image. Thus, transition matrices that represent local Markov models are used as classification features for land cover classification. The classification rule employs only the most important transitions for decision making. The Frobenius inner product is employed as similarity measure. Ten different types of land cover are used for testing the proposed method. In this aspect, the classification performance is significantly high.

Fully Polarimetric Land Cover Classification Based on Hidden Markov Models Trained with Multiple Observations

A land cover classification procedure is presented utilizing the information content of fully polarimetric SAR images. The Cameron coherent target decomposition (CTD) is employed to characterize each pixel, using a set of canonical scattering mechanisms in order to describe the physical properties of the scatterer. The novelty of the proposed classification approach lies on the use of Hidden Markov Models (HMM) to uniquely characterize each type of land cover. The motivation to this approach is the investigation of the alternation between scattering mechanisms from SAR pixel to pixel. Depending on the observations-scattering mechanisms and exploiting the transitions between the scattering mechanisms we decide upon the HMM-land cover type. The classification process is based on the likelihood of observation sequences been evaluated by each model. The performance of the classification approach is assessed my means of fully polarimetric SLC SAR data from the broader area of Vancouver, Canada and was found satisfactory, reaching a success from 87% to over 99%.

Independent and Commutable Target Decomposition of PolSAR Data Using a Mapping From SU(4) to SO(6)

Polarimetric target decomposition is the most commonly used method of extracting information from polarimetric synthetic aperture radar (SAR) images. Coherent target decomposition methods are usually suitable for high-resolution images. Recently, Paladini reviewed coherent target decomposition methods and proposed a new approach, lossless and sufficient target decomposition (LSTD), using the special unitary matrix SU(4). However, this method suffers from parameter dependence and commutation problems that could introduce errors in parameter estimation such as an erroneous odd–even bounce ratio. In order to overcome these problems, a new model to decompose the circular polarization scattering vector is proposed. In this paper, the model applies a mapping from SU(4) to SU(6) to simplify the target representation while meaningful parameters, which are independent, can be extracted. Fully polarimetric L-band UAVSAR data are used to validate the proposed method. The most important odd–even bounce ratio parameter is used to compare the estimation accuracy between the proposed method and LSTD. Results show that the proposed method can extract parameters more accurately.

Polarimetric decomposition for the selection of persistent scatterers

ABSTRACTIn this paper, the issue of employing polarimetric processing methods for persistent scatterers (PS) selection is addressed. The scope of the paper regards the processing methods for PS generation by means of coherent target decomposition (CTD). The key is to exploit scatterer physical characteristics, besides scatterer coherence and stability, so as to classify them as PS utilizing as less data sets as possible. Cameron’s CTD is proposed for PS classification based on scatterers physical properties as an alternative to polarization phase difference (PPD) for it can eliminate the scatterer initial orientation angle. Additionally, provides a complete set of elemental scattering mechanisms in order to describe the physical properties of the scatterer, and thus create a more reliable PS net. These assets make Cameron’s CTD outperform PPD in exploiting scatterer physical properties for PS selection. The study also focuses on minimizing the number of used data sets for PS net generation, which led to experimenting with only two fully polarimetric SAR data sets. The results are promising enough to establish the proposed method as an auxiliary tool to be utilized prior to any classic interferometric techniques for early scene evaluation in order to decide whether the scene would be fruitful for applying permanent scatterer interferometry or not.

Mitigation of Reflection Symmetry Assumption and Negative Power Problems for the Model-Based Decomposition

The assumption of reflection symmetry is one of the major deficiencies for model-based decompositions. The introduction of helix scattering components can mitigate some of the impact of this assumption while simultaneously generating some new negative power problems. The helix mechanism has been developed in the Krogager coherent target decomposition where there is no negative power problem. Why do these problems happen in model-based decompositions? In this paper, we review the Krogager decomposition based on the coherency matrix form of the unified CTD model and come to the conclusion that the imaginary part of $T_{23}$ is an overestimate of the helix contribution, which is the main cause of the problem. Furthermore, we review the techniques of symmetric scatterer transformation in the Cameron and Huynen decompositions and expand them to the multilook coherency matrix. Two new models of the nonlinear programming problem are proposed to get the transformed coherency matrix: 1) by subtracting the asymmetric component; and 2) by the procedure of con-diagonalization. A new parameter is established to measure the degree of reflection asymmetry of the coherency matrices. Experimental results in the L-band Airborne SAR (AIRSAR) San Francisco data show that the two new models can reduce the correlations between copolarized and cross-polarized channels significantly. In this case, the assumption of reflection symmetry is reasonable for the transformed coherency matrices, and negative power problems related to helix scattering no longer exist in the model-based decompositions; hence, the helix scattering component is not needed. Lastly, the Freeman and Durden decomposition has been applied to the transformed coherency matrices. Experimental results showed that the decomposition results can reflect the predominant characteristics of the ground objects.

Polarimetric Simulations of SAR at L-Band Over Bare Soil Using Scattering Matrices of Random Rough Surfaces From Numerical Three-Dimensional Solutions of Maxwell Equations

We have performed simulations of random rough surface scattering using 3-D numerical solution of Maxwell equations (NMM3D) using surface size up to 32 $\times$ 32 squared wavelengths. The rough surfaces are characterized by exponential correlation functions. The simulation results of cross- and copolarization backscattering coefficients were in good agreement with experimental measurements of bare soils at L-band. Because in numerical solutions of Maxwell equations the electric fields of the scattered wave are calculated for each realization, scattering matrices can be simulated by NMM3D, and such simulations are performed in this paper. For a given RMS height, correlation length, soil permittivity, and incident angle, we calculated the radar scattering matrix up to 958 independent realizations. For each realization, the components of the scattering matrix, namely, $S_{\rm HH}$ , $S_{\rm VV}$ , $S_{\rm HV}$ , and $S_{\rm VH}$ , are calculated. Using the simulated scattering matrices, we calculate the polarimetric speckle statistics (amplitude and phase difference), followed by a comparison with theoretical distributions. For fully developed speckle from the homogeneous rough surface, the results are examined and validated to ensure the simulated data quality as far as polarimetric properties are concerned. By taking ensemble averages, we calculate the coherency matrix from which the eigenvalues, entropy, anisotropy, and alpha angle in coherent target decomposition are then calculated. In particular, characterization of polarimetric descriptors for rough surface is presented. Issues of scattering symmetry characteristics are also discussed.

Quantitative data analysis of ESAR data

Abstract. A synthetic aperture radar (SAR) data processing uses the backscattered electromagnetic wave to map radar reflectivity of the ground surface. The polarization property in radar remote sensing was used successfully in many applications, especially in target decomposition. This paper presents a case study to the experiments which are performed on ESAR L-Band full polarized data sets from German Aerospace Center (DLR) to demonstrate the potential of coherent target decomposition and the possibility of using the weather radar measurement parameter, such as the differential reflectivity and the linear depolarization ratio to obtain the quantitative information of the ground surface. The raw data of ESAR has been processed by the SAR simulator developed using MATLAB program code with Range-Doppler algorithm.

A unified three-component scattering model for polarimetric coherent target decomposition

In this article, first, the properties of the symmetric scattering space are examined based on the representation of the maximum symmetric scattering component proposed by Touzi. The symmetric scattering unit disc in the complex plane is mapped to a more intuitive unit sphere representation, and a new simpler classification method for symmetric scatterers is proposed based on a simplified symmetric scatterer scattering matrix metric distance. Then, a unified three-component scattering model for polarimetric coherent target decomposition (CTD) is proposed. Pauli decomposition, sphere, dihedral, helix (SDH) decomposition and Cameron decomposition can be expressed as a simplified form of the unified model with some certain restrictions, respectively. In addition, SDH decomposition and Cameron decomposition are proved to be equivalent by verification of restrictions’ consistency. Finally, by the combination of SDH decomposition and symmetric scatterer classification, a scattering matrix classification scheme is proposed to classify the scatterers into 12 scattering types.

Equivalence Between Cameron's Unit Disc and PoincarÉ's Sphere for Symmetric Scattering Characterization and Classification

Cameron's coherent target decomposition and classification is able to represent a symmetric scatterer onto a unit disc in the complex plane, and assign it to one of the six symmetrical elemental scatterer classes. Recently, Touzi et al. proposed a variation of Cameron's method by introducing a coherent analysis. Moreover the Poincare's sphere, was used instead of the unit disc for representing symmetric scattering because it was considered a more suitable domain. The aim of this work is to demonstrate the equivalence of using Poincare's sphere domain and Cameron's unit disc, in term of characterisation and classification of symmetric scattering types.

Speckle effect on polarimetric target scattering decomposition of SAR imagery

Target scattering decompositions have become the standard tools for scattering classification and for target geophysical parameter extraction from polarimetric synthetic aperture radar (SAR) data. Incoherent decompositions are based on the characteristic decomposition of the target coherency matrix and the use of its eigenvalues and eigenvector parameters for a unique characterization of target scattering. The presence of speckle, however, may introduce biases to the incoherent parameters derived from the characteristic decomposition of the coherency matrix sample. In this study, it is shown that both coherency eigenvalues and eigenvector parameters estimates may be significantly biased, with a bias that decreases with an increase in the size of the coherency sample processing window. The statistics of cross-channel coherence we have previously derived for circular complex Gaussian processes can be used to determine the minimum processing window size required for unbiased estimation of the incoherent decomposition parameters. The speckle effect on coherent target decomposition is also investigated, and the implications for the use of coherent versus incoherent decomposition are discussed.

Characterization of target symmetric scattering using polarimetric SARs

Cameron's coherent target decomposition (CTD) theory and the classification method that Cameron developed for operational use of his CTD are reconsidered. It is shown that Cameron's classification leads to a coarse scattering segmentation because of the large class dispersion that corresponds to a synthetic aperture radar (SAR) system with about /spl plusmn/8-dB channel imbalance. The application of Cameron's method within known SAR radiometric calibration requirements limits the utility of the classification. In addition, Cameron's classification is applied under the implicit assumption on the coherence nature of target scattering, and this might yield erroneous results within areas of noncoherent scattering. A new method, named the symmetric scattering characterization method (SSCM), is introduced to better exploit the information provided by the largest target symmetric scattering component in the context of coherent scattering. The Poincare/spl acute/ sphere is used as the basis for a more complete representation of symmetric scattering than Cameron's unit disk, thus enabling the SSCM to generate better segmentation of target symmetric scattering with much higher resolution. In order to limit the application of the SSCM to targets of coherent scattering, new methods are developed for assessment and validation of the coherent nature of point and extended target scattering.