1,266 research outputs found
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
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
Polarimetric Guided Nonlocal Means Covariance Matrix Estimation for Defoliation Mapping
In this study we investigate the potential for using synthetic aperture radar
(SAR) data to provide high resolution defoliation and regrowth mapping of trees
in the tundra-forest ecotone. Using aerial photographs, four areas with live
forest and four areas with dead trees were identified. Quad-polarimetric SAR
data from RADARSAT-2 was collected from the same area, and the complex
multilook polarimetric covariance matrix was calculated using a novel extension
of guided nonlocal means speckle filtering. The nonlocal approach allows us to
preserve the high spatial resolution of single-look complex data, which is
essential for accurate mapping of the sparsely scattered trees in the study
area. Using a standard random forest classification algorithm, our filtering
results in over classification accuracy, higher than traditional
speckle filtering methods, and on par with the classification accuracy based on
optical data.Comment: Update to match final submitted version accepted to IGARSS 2020. 4
pages, 2 columns, 3 figure
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