deposit_your_work

Unsupervised SAR Image Segmentation using Recursive Partitioning

Files in this item

Files Size Format View
Ven2000Apr5Unsupervi.PDF 435.4Kb application/pdf Thumbnail
Ven2000Apr5Unsupervi.PS 2.769Mb application/postscript View/Open

Show simple item record

Item Metadata

dc.contributor.author Baraniuk, Richard G.
dc.creator Baraniuk, Richard G.
dc.date.accessioned 2007-10-31T01:08:06Z
dc.date.available 2007-10-31T01:08:06Z
dc.date.issued 2000-04-01
dc.date.submitted 2000-04-01
dc.identifier.uri http://hdl.handle.net/1911/20414
dc.description Conference Paper
dc.description.abstract We present a new approach to SAR image segmentation based on a Poisson approximation to the SAR amplitude image. It has been established that SAR amplitude images are well approximated using Rayleigh distributions. We show that, with suitable modifications, we can model piecewise homogeneous regions (such as tanks, roads, scrub, etc.) within the SAR amplitude image using a Poisson model that bears a known relation to the underlying Rayleigh distribution. We use the Poisson model to generate an efficient tree-based segmentation algorithm guided by the minimum description length (MDL) criteria. We present a simple fixed tree approach, and a more flexible adaptive recursive partitioning scheme. The segmentation is unsupervised, requiring no prior training, and very simple, efficient, and effective for identifying possible regions of interest (targets). We present simulation results on MSTAR clutter data to demonstrate the performance obtained with this parsing technique.
dc.language.iso eng
dc.subject segmentation
multiscale
wavelets
MDL
SAR
ATR
MSTAR
dc.subject.other Wavelet based Signal/Image Processing
dc.title Unsupervised SAR Image Segmentation using Recursive Partitioning
dc.type Conference Paper
dc.date.note 2004-01-08
dc.citation.bibtexName inproceedings
dc.date.modified 2006-07-05
dc.contributor.center Digital Signal Processing (http://dsp.rice.edu/)
dc.subject.keyword segmentation
multiscale
wavelets
MDL
SAR
ATR
MSTAR
dc.citation.volumeNumber 4053
dc.citation.location Orlando, FL
dc.citation.conferenceName SPIE Symp. OE/Aerospace Sensing and Dual Use Photonics, Algorithm for Synthetic Aperture Radar Image
dc.type.dcmi Text
dc.identifier.citation R. G. Baraniuk,"Unsupervised SAR Image Segmentation using Recursive Partitioning," in SPIE Symp. OE/Aerospace Sensing and Dual Use Photonics, Algorithm for Synthetic Aperture Radar Image,

This item appears in the following Collection(s)

  • ECE Publications [1045 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.