2,283 research outputs found
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
Extrapolation of Airborne Polarimetric and Interferometric SAR Data for Validation of Bio-Geo-Retrieval Algorithms for Future Spaceborne SAR Missions
Spaceborne SAR system concepts and mission design is often based on algorithms developed and the experience gathered
from airborne SAR experiments and associated dedicated campaigns. However, airborne SAR systems have better
performance parameters than their future space-borne counterparts as their design is not impacted by mass, power, and
storage constraints.
This paper describes a methodology to extrapolate spaceborne quality SAR image products from long wavelength airborne
polarimetric SAR data which were acquired especially for the development and validation of bio/geo-retrieval algorithms in
forested regions. For this purpose not only system (sensor) related parameters are altered, but also those relating to the
propagation path (ionosphere) and to temporal decorrelation
Analytic Expressions for Stochastic Distances Between Relaxed Complex Wishart Distributions
The scaled complex Wishart distribution is a widely used model for multilook
full polarimetric SAR data whose adequacy has been attested in the literature.
Classification, segmentation, and image analysis techniques which depend on
this model have been devised, and many of them employ some type of
dissimilarity measure. In this paper we derive analytic expressions for four
stochastic distances between relaxed scaled complex Wishart distributions in
their most general form and in important particular cases. Using these
distances, inequalities are obtained which lead to new ways of deriving the
Bartlett and revised Wishart distances. The expressiveness of the four analytic
distances is assessed with respect to the variation of parameters. Such
distances are then used for deriving new tests statistics, which are proved to
have asymptotic chi-square distribution. Adopting the test size as a comparison
criterion, a sensitivity study is performed by means of Monte Carlo experiments
suggesting that the Bhattacharyya statistic outperforms all the others. The
power of the tests is also assessed. Applications to actual data illustrate the
discrimination and homogeneity identification capabilities of these distances.Comment: Accepted for publication in the IEEE Transactions on Geoscience and
Remote Sensing journa
Range-Point Migration-Based Image Expansion Method Exploiting Fully Polarimetric Data for UWB Short-Range Radar
Ultrawideband radar with high-range resolution is a promising technology for use in short-range 3-D imaging applications, in which optical cameras are not applicable. One of the most efficient 3-D imaging methods is the range-point migration (RPM) method, which has a definite advantage for the synthetic aperture radar approach in terms of computational burden, high accuracy, and high spatial resolution. However, if an insufficient aperture size or angle is provided, these kinds of methods cannot reconstruct the whole target structure due to the absence of reflection signals from large part of target surface. To expand the 3-D image obtained by RPM, this paper proposes an image expansion method by incorporating the RPM feature and fully polarimetric data-based machine learning approach. Following ellipsoid-based scattering analysis and learning with a neural network, this method expresses the target image as an aggregation of parts of ellipsoids, which significantly expands the original image by the RPM method without sacrificing the reconstruction accuracy. The results of numerical simulation based on 3-D finite-difference time-domain analysis verify the effectiveness of our proposed method, in terms of image-expansion criteria
Change detection in SAR time-series based on the coefficient of variation
This paper discusses change detection in SAR time-series. Firstly, several
statistical properties of the coefficient of variation highlight its pertinence
for change detection. Then several criteria are proposed. The coefficient of
variation is suggested to detect any kind of change.
Then other criteria based on ratios of coefficients of variations are
proposed to detect long events such as construction test sites, or point-event
such as vehicles.
These detection methods are evaluated first on theoretical statistical
simulations to determine the scenarios where they can deliver the best results.
Then detection performance is assessed on real data for different types of
scenes and sensors (Sentinel-1, UAVSAR). In particular, a quantitative
evaluation is performed with a comparison of our solutions with
state-of-the-art methods
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