Multidimensional SAR data analysis based on binary partition trees and the covariance matrix geometry
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Document typeConference report
Defense date2014
Rights accessRestricted access - publisher's policy
Abstract
In this paper, we propose the use of the Binary
Partition Tree (BPT) as a region-based and multi-scale image
representation to process multidimensional SAR data, with special
emphasis on polarimetric SAR data. We also show that
this approach could be extended to other types of remote
sensing imaging technologies, such as hyperspatial imagery. The
Binary Partition Tree contains a lot of information about the
image structure at different detail levels. At the same time, this
structure represents a convenient vehicle to exploit both the
statistical properties, as well as the geometric properties of the
multidimensional SAR data given by the covariance matrix. The
BPT construction process and its exploitation for PolSAR and
temporal data information estimation is analyzed in this work. In
particular, this work focuses on the speckle noise filtering problem
and the temporal characterization of the image dynamics. Results
with real data are presented to illustrate the capabilities of
the BPT processing approach, specially to maintain the spatial
resolution and the small details of the image.
CitationAlonso, A. [et al.]. Multidimensional SAR data analysis based on binary partition trees and the covariance matrix geometry. A: International Radar Conference. "International Radar Conference 2014: catching the invisible : 13-17 October 2014, Ille, France". Lille: 2014.
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