Abstract
We study the segmentation of SAR imagery using wavelet-domain Hidden Markov Tree (HMT) models. The HMT model is a tree-structured probabilistic graph that captures the statistical properties of the wavelet transforms of images. This technique has been successfully applied to the segmentation of natural texture images, documents, etc. However, SAR image segmentation poses a difficult challenge owing to the high levels of speckle noise present at fine scales. We solve this problem using a 'truncated' wavelet HMT model specially adapted to SAR images. This variation is built using only the coarse scale wavelet coefficients. When applied to SAR images, this technique provides a reliable initial segmentation. We then refine the classification using a multiscale fusion technique, which combines the classification information across scales from the initial segmentation to correct for misclassifications. We provide a fast algorithm, and demonstrate its performance on MSTAR clutter data.
Original language | English (US) |
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Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
Publisher | Society of Photo-Optical Instrumentation Engineers |
Pages | 110-120 |
Number of pages | 11 |
Volume | 4053 |
State | Published - 2000 |
Event | Algorithms for Synthetic Aperture Radar Imagery VII - Orlando, FL, USA Duration: Apr 24 2000 → Apr 28 2000 |
Other
Other | Algorithms for Synthetic Aperture Radar Imagery VII |
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City | Orlando, FL, USA |
Period | 4/24/00 → 4/28/00 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics