649 research outputs found
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
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
On the use of the l(2)-norm for texture analysis of polarimetric SAR data
In this paper, the use of the l2-norm, or Span, of the scattering vectors is suggested for texture analysis of polarimetric synthetic aperture radar (SAR) data, with the benefits that we need neither an analysis of the polarimetric channels separately nor a filtering of the data to analyze the statistics. Based on the product model, the distribution of the l2-norm is studied. Closed expressions of the probability density functions under the assumptions of several texture distributions are provided. To utilize the statistical properties of the l2-norm, quantities including normalized moments and log-cumulants are derived, along with corresponding estimators and estimation variances. Results on both simulated and real SAR data show that the use of statistics based on the l2-norm brings advantages in several aspects with respect to the normalized intensity moments and matrix variate log-cumulants.Peer ReviewedPostprint (published version
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
A Study on Utilization of Polarimetric SAR Data in Planning a Smart City
AbstractIn the present world, there is a huge requirement for a truly efficient city not only operating in an integrated mode, but also to optimize the resources of each system to have better eco-friendly livelihood. Currently, this novel concept has led to the establishment of smart city with integration among informational and operational efficiency. With recent advances in remote sensing especially in the field of Polarimetric Synthetic Aperture Radar (SAR) data, using suitable polarimetric target decomposition techniques, data can be classified for further utilization in remote sensing applications. As a part of this exploration, a study has been taken to understand the utilisation of polarimetric data in building a smart city by exploiting the available resources in a given urban area. Different types of polarimetric decomposition techniques are applied on the data along with polarimetric speckle filters where classification of targets is performed based on the scattering mechanism of the polarized wave with each target in the scene. Encouraging preliminary results were obtained in the study using polarimetric SAR data adding another dimension in planning a smart city
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