Research Of Model-based Polarimetric SAR Decomposition Constrained For Nonnegative Eigenvalues

As an active remote sensing tool, polarimetric Synthetic Aperture Radar (PolSAR) is with "all weather", day and night imaging capabilities. Its resolution is generally higher than that of real aperture radar. In recent years, it began to be widely used in the field of military, surveying and mapping, agriculture, forestry, geology et al. As an important information extracting method, polarimetric decomposition, especially incoherent model-based decomposition, is one of the most active research directions in PolSAR field. It can get the powers and parameters of different scattering mechanisms, hence being used for PolSAR image classification, SAR interferometry, speckle filtering, soil moisture and surface roughness estimation, and so on.Since Freeman and Durden proposed the three-component decomposition, more than20incoherent model-based decompositions have been published. Although many successful applications have been achieved, several severe problems exist, including violating non-negative eigenvalue constraint (NNEC), occurrence of negative powers, overestimation of volume scattering power, insufficient utilization of polarimetric information, employing coherent models for ground scattering hence being incapable of describing depolarizing effect, the confusion between forests and buildings whose orientation is unparallel to SAR azimuth direction, etc. In general, real data is used to verify the decomposition results in experiment, so ground truth is lack and quantitative evaluations of the accuracy of decomposed powers and other parameters are difficult.To solve these problems, a simulation framework on the basis of model-based incoherent decomposition was first established in this paper. By simulating parameters of all components, computing corresponding reflection asymmetric scattering models, the power-weighted coherency matrix [T] were obtained. With simulated data, we are able to quantitatively compare the results given by diverse decompositions with simulation parameters. Simulated [T] with different dominant mechanisms were specially selected to better model the real conditions.Two highly adaptive decompositions were proposed in this paper. Both methods perform deorientation processing, apply NNEC to the calculation of the parameters of helix scattering and volume scattering, utilize Neumann’s scattering model and dipole to describe volume scattering, and select the parameters that let volume scattering explain the most cross-polarized power. But the first decomposition computes volume scattering parameters without reflection symmetry assumption (denote this decomposition as RAVD). As a result, generally, helix scattering and volume scattering cannot explain all cross-polarized power. Therefore, apply Neumann’s depolarizing model to describe the dominant ground scattering to explain the remaining cross-polarized power and coheren model to describe the secondary ground scattering. The second decomposition computes volume scattering parameters with reflection symmetry assumption (denote this decomposition as RSVD). In most areas dominated by surface scattering and double-bounce scattering, cross-polarized power is explained by helix scattering and volume scattering, then we can get the parameters of surface scattering and double-bounce via van Zyl decomposition. However, in some forests, a small proportion of cross-polarized power cannot be explained by volume scattering and helix scattering. In this case, perform a three-component decomposition to the observed coherency matrix, during which volume scattering and the dominant ground scattering are both modeled by Neumann’s reflection symmetric model. If desirable results cannot be achieved, then perform a three-component reflection asymmetric decomposition to the observed coherency matrix.Experiments using simulated data and UAVSAR data revealed that, in most cases, all elements in the observed coherency matrix except T13can be well fitted by both decompositions. If three-component reflection asymmetric decomposition is implemented, it is likely to fit all the elements in the observed coherency matrix except the imaginary part of T13. RSVD completely avoids the occurrence of negative powers, meanwhile, in the results of RAVD, the proportion of negative powers is lower than0.070%. Compared with several latest Nonnegative Eigenvalue decompositions, the two proposed decompositions significantly lower the estimation of volume scattering power and better estimate component powers. In most instances, RAVD performs better than RSVD in estimating orientation angle randomness and complex scattering coefficients of different components. It is also worth noticing that in the decomposition results of RAVD, forests and the buildings whose orientation direction is unparallel to SAR azimuth direction can be easily separated. But in the areas dominated by surface scattering or double-bounce, RSVD better estimate component powers than RAVD.An unsupervised scattering mechanism classifier was advanced based on powers of scattering mechanisms. Scattering mechanism classes are defined as the combinations of dominant and secondary mechanisms. Through analysis of the characteristics of several typical mechanisms alone and mixture of two mechanisms, a classifier on the basis of characteristics and segmentation of characteristic space, was given. Since the proposed classifier is nearly free of polarimetric decomposition, it avoids the overestimation of volume scattering power and eigenvalue decomposition. The proposed classifier has much higher efficiency than Wishart-H/alpha classifier and fuzzy H/alpha classifier, and could provide secondary mechanism. The proposed classifier can be used for a fast classification of PolSAR images, or as a pre-classification step of sophisticated classifier. It also has the potential to simplify model-based incoherent decomposition.In experiments with simulated data, the Kappa coefficient by the proposed method was0.864. It performed much better than H/alpha classifier, Wishart-H/alpha classifier as well as fuzzy H/alpha classifier in the identification of dominant scattering mechanism. It was demonstrated by UAVSAR data that the proposed classifier was able to identify dominant and secondary mechanism in forests and urban areas.