Back to Search
Start Over
Despeckling of Synthetic Aperture Radar Images Using Monte Carlo Texture Likelihood Sampling.
- Source :
- IEEE Transactions on Geoscience & Remote Sensing; Feb2014, Vol. 52 Issue 2, p1238-1248, 11p
- Publication Year :
- 2014
-
Abstract
- Speckle noise is found in synthetic aperture radar (SAR) images and can affect visualization and analysis. A novel stochastic texture-based algorithm is proposed to suppress speckle noise while preserving the underlying structural and texture detail. Based on a sorted local texture model and a Fisher-Tippett logarithmic-space speckle distribution model, a Monte Carlo texture likelihood sampling strategy is proposed to estimate the true signal. The algorithm is compared to six other classic and state-of-the-art despeckling techniques. The comparison is performed both on synthetic noisy images added and on actual SAR images. Using peak signal-to-noise ratio, contrast-to-noise ratio, and structural similarity index as image quality metrics, the proposed algorithm shows strong despeckling performance when compared to existing despeckling algorithms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 52
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 101186509
- Full Text :
- https://doi.org/10.1109/TGRS.2013.2248739