Polarimetric Radar Target Decomposition and Classification

The polarization transformation properties of radar target characterize the radar scattering phenomena according to the theory introduced by E. Kennaugh, J. R. Huynen and S. R. Cloude. For this reason airborne and spaceborne polarimetric SAR are currently emergent technologies useful for providing maximum available information for application on remote sensing. The measurable quantities in the linear polarization basis have strong variability as function of the target aspect angle given the presence of strong disturbance effects like multiplicative noise. For this reason and given the difficult interpretation of the scattering coefficients in linear polarization basis, many authors have proposed several target decomposition theorems in order to give a likelihood interpretation of the observed phenomena. Target decomposition theorems have shown the basis for developing target classification and remote sensing inversion studies. In this Philosophy Doctoral dissertation the Einstein's photon circular polarization special unitary basis is used extensively in order to develop several lossless and sufficient target decomposition theorems providing orientation invariant parameters stressing the proper number of degrees of freedom of each case. Respectively the five novel target decomposition theorems proposed in this Dissertation are proven useful for: a) extracting the characteristic parameters of a coherent field, b) characterizing the statistics of a random field, c) decomposing the main features of a reciprocal deterministic target vector, d) modeling the degrees of freedom of a random reciprocal target, e) assessing the features of circular polarization dual coherent radar. The quantum theory of radar target scattering has been introduced by analyzing qualitatively the photon spin transformation properties of some elemental targets. Two Unsupervised Classification schemes based on the inner Hermitian product have been proposed generalizing the Cameron's approach to not symmetric and random targets. A Supervised Classification scheme based on the Cloude-Pottier eigen-features has been proposed for the identification of man-made target. A relationship of equivalence for the estimation of the coherency matrices of random target is also proved. Results have been validated via an extensive use of airborne and spaceborne fully polarimetric, simulated dual coherent radar and some anechoic chamber data sets. The new parameters proposed in this Ph. D. dissertation and the wide number of classes proposed are useful for assessing the advantages of fully polarimetric system versus dual coherent radar radiating circular polarization

Polarimetric radar processing of AIRSAR imagery from Los Angeles basin region

Title from PDF of title page (University of Missouri--Columbia, viewed on February 22, 2011).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. Justin J. Legarsky.M. S. University of Missouri--Columbia 2009.Extracting useful information and intelligence from polarimetric interferometric synthetic aperture radar (PolInSAR) data involves a variety of highly sophisticated processing methods. To aid in the advancement of efficient PolInSAR processing techniques, an investigation of underlying scattering mechanisms such as coherent scatterers (CS) and polarimetric decomposition techniques is conducted in this study using JPL AIRSAR fully polarimetric data over a portion of the greater Los Angeles area. For this study, selection of the overall optimum polarization showed an increase of CS candidates compared to standard polarizations. In addition, polarimetric decomposition ([alpha]-H and F/D) analysis of CS and non-CS (NCS) pixels found a trend of increasing double-bounce scattering, Fd, with decreasing volume scattering, Fv, and polarimetric Entropy, H, for CS relative to NCS.Includes bibliographical references

Modifying the Yamaguchi Four-Component Decomposition Scattering Powers Using a Stochastic Distance

Model-based decompositions have gained considerable attention after the initial work of Freeman and Durden. This decomposition which assumes the target to be reflection symmetric was later relaxed in the Yamaguchi et al. decomposition with the addition of the helix parameter. Since then many decomposition have been proposed where either the scattering model was modified to fit the data or the coherency matrix representing the second order statistics of the full polarimetric data is rotated to fit the scattering model. In this paper we propose to modify the Yamaguchi four-component decomposition (Y4O) scattering powers using the concept of statistical information theory for matrices. In order to achieve this modification we propose a method to estimate the polarization orientation angle (OA) from full-polarimetric SAR images using the Hellinger distance. In this method, the OA is estimated by maximizing the Hellinger distance between the un-rotated and the rotated $T_{33}$ and the $T_{22}$ components of the coherency matrix $\mathbf{[T]}$. Then, the powers of the Yamaguchi four-component model-based decomposition (Y4O) are modified using the maximum relative stochastic distance between the $T_{33}$ and the $T_{22}$ components of the coherency matrix at the estimated OA. The results show that the overall double-bounce powers over rotated urban areas have significantly improved with the reduction of volume powers. The percentage of pixels with negative powers have also decreased from the Y4O decomposition. The proposed method is both qualitatively and quantitatively compared with the results obtained from the Y4O and the Y4R decompositions for a Radarsat-2 C-band San-Francisco dataset and an UAVSAR L-band Hayward dataset.Comment: Accepted for publication in IEEE J-STARS (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Ship and Oil-Spill Detection Using the Degree of Polarization in Linear and Hybrid/Compact Dual-Pol SAR

Monitoring and detection of ships and oil spills using synthetic aperture radar (SAR) have received a considerable attention over the past few years, notably due to the wide area coverage and day and night all-weather capabilities of SAR systems. Among different polarimetric SAR modes, dual-pol SAR data are widely used for monitoring large ocean and coastal areas. The degree of polarization (DoP) is a fundamental quantity characterizing a partially polarized electromagnetic field, with significantly less computational complexity, readily adaptable for on-board implementation, compared with other well-known polarimetric discriminators. The performance of the DoP is studied for joint ship and oil-spill detection under different polarizations in hybrid/compact and linear dual-pol SAR imagery. Experiments are performed on RADARSAT-2 -band polarimetric data sets, over San Francisco Bay, and -band NASA/JPL UAVSAR data, covering the Deepwater Horizon oil spill in the Gulf of Mexico

Coherency Matrix Decomposition-Based Polarimetric Persistent Scatterer Interferometry

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The rationale of polarimetric optimization techniques is to enhance the phase quality of the interferograms by combining adequately the different polarization channels available to produce an improved one. Different approaches have been proposed for polarimetric persistent scatterer interferometry (PolPSI). They range from the simple and computationally efficient BEST, where, for each pixel, the polarimetric channel with the best response in terms of phase quality is selected, to those with high-computational burden like the equal scattering mechanism (ESM) and the suboptimum scattering mechanism (SOM). BEST is fast and simple, but it does not fully exploit the potentials of polarimetry. On the other side, ESM explores all the space of solutions and finds the optimal one but with a very high-computational burden. A new PolPSI algorithm, named coherency matrix decomposition-based PolPSI (CMD-PolPSI), is proposed to achieve a compromise between phase optimization and computational cost. Its core idea is utilizing the polarimetric synthetic aperture radar (PolSAR) coherency matrix decomposition to determine the optimal polarization channel for each pixel. Three different PolSAR image sets of both full- (Barcelona) and dual-polarization (Murcia and Mexico City) are used to evaluate the performance of CMD-PolPSI. The results show that CMD-PolPSI presents better optimization results than the BEST method by using either $D_{\mathrm{ A}}$ or temporal mean coherence as phase quality metrics. Compared with the ESM algorithm, CMD-PolPSI is 255 times faster but its performance is not optimal. The influence of the number of available polarization channels and pixel's resolutions on the CMD-PolPSI performance is also discussed.Peer ReviewedPostprint (author's final draft

Optimum graph cuts for pruning binary partition trees of polarimetric SAR images

This paper investigates several optimum graph-cut techniques for pruning binary partition trees (BPTs) and their usefulness for the low-level processing of polarimetric synthetic aperture radar (PolSAR) images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a binary tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for a large number of applications. Many of these applications consist in populating the tree with a specific feature and in applying a graph cut called pruning to extract a partition of the space. In this paper, different pruning examples involving the optimization of a global criterion are discussed and analyzed in the context of PolSAR images for segmentation. Through the objective evaluation of the resulting partitions by means of precision-and-recall-for-boundaries curves, the best pruning technique is identified, and the influence of the tree construction on the performances is assessed.Peer ReviewedPostprint (author's final draft