324 research outputs found
Improvement of PolSAR Decomposition Scattering Powers Using a Relative Decorrelation Measure
In this letter, a methodology is proposed to improve the scattering powers
obtained from model-based decomposition using Polarimetric Synthetic Aperture
Radar (PolSAR) data. The novelty of this approach lies in utilizing the
intrinsic information in the off-diagonal elements of the 33 coherency
matrix represented in the form of complex correlation
coefficients. Two complex correlation coefficients are computed between
co-polarization and cross-polarization components of the Pauli scattering
vector. The difference between modulus of complex correlation coefficients
corresponding to (i.e. the degree of polarization
(DOP) optimized coherency matrix), and (original) matrices is
obtained. Then a suitable scaling is performed using fractions \emph{i.e.,}
obtained
from the diagonal elements of the matrix.
Thereafter, these new quantities are used in modifying the Yamaguchi
4-component scattering powers obtained from . To
corroborate the fact that these quantities have physical relevance, a
quantitative analysis of these for the L-band AIRSAR San Francisco and the
L-band Kyoto images is illustrated. Finally, the scattering powers obtained
from the proposed methodology are compared with the corresponding powers
obtained from the Yamaguchi \emph{et. al.,} 4-component (Y4O) decomposition and
the Yamaguchi \emph{et. al.,} 4-component Rotated (Y4R) decomposition for the
same data sets. The proportion of negative power pixels is also computed. The
results show an improvement on all these attributes by using the proposed
methodology.Comment: Accepted for publication in Remote Sensing Letter
Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing Technique
Polarization characterizes the vector state of EM wave. When interacting with polarized wave, rough natural surface often induces dominant surface scattering; building also presents dominant double-bounce scattering. Tsunami/earthquake causes serious destruction just by inundating the land surface and destroying the building. By analyzing the change of surface and double-bounce scattering before and after disaster, we can achieve a monitoring of damages. This constitutes one basic principle of polarimetric microwave remote sensing of tsunami/earthquake. The extraction of surface and double-bounce scattering from coherency matrix is achieved by model-based decomposition. The general four-component scattering power decomposition with unitary transformation (G4U) has been widely used in the remote sensing of tsunami/earthquake to identify surface and double-bounce scattering because it can adaptively enhance surface or double-bounce scattering. Nonetheless, the strict derivation in this chapter conveys that G4U cannot always strengthen the double-bounce scattering in urban area nor strengthen the surface scattering in water or land area unless we adaptively combine G4U and its duality for an extended G4U (EG4U). Experiment on the ALOS-PALSAR datasets of 2011 great Tohoku tsunami/earthquake demonstrates not only the outperformance of EG4U but also the effectiveness of polarimetric remote sensing in the qualitative monitoring and quantitative evaluation of tsunami/earthquake damages
General model-based decomposition framework for polarimetric SAR images
2017 Spring.Includes bibliographical references.Polarimetric synthetic aperture radars emit a signal and measure the magnitude, phase, and polarization of the return. Polarimetric decompositions are used to extract physically meaningful attributes of the scatterers. Of these, model-based decompositions intend to model the measured data with canonical scatter-types. Many advances have been made to this field of model-based decomposition and this work is surveyed by the first portion of this dissertation. A general model-based decomposition framework (GMBDF) is established that can decompose polarimetric data with different scatter-types and evaluate how well those scatter-types model the data by comparing a residual term. The GMBDF solves for all the scatter-type parameters simultaneously that are within a given decomposition by minimizing the residual term. A decomposition with a lower residual term contains better scatter-type models for the given data. An example is worked through that compares two decompositions with different surface scatter-type models. As an application of the polarimetric decomposition analysis, a novel terrain classification algorithm of polSAR images is proposed. In the algorithm, the results of state-of-the-art polarimetric decompositions are processed for an image. Pixels are then selected to represent different terrain classes. Distributions of the parameters of these selected pixels are determined for each class. Each pixel in the image is given a score according to how well its parameters fit the parameter distributions of each class. Based on this score, the pixel is either assigned to a predefined terrain class or labeled unclassified
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 and the components of the coherency matrix
. Then, the powers of the Yamaguchi four-component model-based
decomposition (Y4O) are modified using the maximum relative stochastic distance
between the and the 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
Interpretation of the coherency matrix for three-dimensional polarization states
From an appropriate parameterization of the three-dimensional (3D) coherency
matrix R, that characterizes the second-order, classical states of
polarization, the coherency matrices are classified and interpreted in terms of
incoherent decompositions. The relevant physical quantities derived from R, as
the intensity, the degree of polarimetric purity, the indices of polarimetric
purity, the angular momentum, the degree of directionality and the degree of
linear polarization are identified and interpreted on the light of the case
study performed. The information provided by R about the direction of
propagation is clarified and it is found that coherency matrices with, does not
always represent states with a well-defined direction of propagation. Moreover,
it is demonstrated the existence of 3D mixed states that cannot be decomposed
into a superposition of a pure state, a 2D unpolarized state, and a 3D
unpolarized state. Appropriate representation and interpretation for all the
different types of 3D coherency matrices is provided through physical
consistent criteria. Under the approach proposed, the conventional
two-dimensional model arises naturally.Comment: 24 pages, 11 figure
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 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
Target scattering characterization in sar polarimetry using model-free approaches
Target decomposition methods for polarimetric Synthetic Aperture Radar (PolSAR) data aim at explaining the scattering information. In this regard, several conventional model-based methods use scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branching conditions, leading to several limitations. This study uses the 3D Barakat degree of polarization (DoP) to obtain the scattered wave polarization state. We employ the DoP to obtain the even bounce, odd-bounce, and diffuse scattering power components. Besides, we propose a measure of target scattering asymmetry, which is subsequently utilized to obtain the helicity power. All the power components in our approach are roll-invariant and non-negative, and the decomposition preserves the total power. We utilized C-band full polarimetric RADARSAT-2 data to show the effectiveness of the proposed decomposition. © 2021 IEEE.Peer ReviewedPostprint (author's final draft
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