Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 1137–1140, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-1137-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 1137–1140, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-1137-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Oct 2019

19 Oct 2019

SPECKLE REDUCTION IN SAR IMAGES USING A BAYESIAN MULTISCALE APPROACH

F. Zakeri1, M. R. Saradjian1, and M. R. Sahebi2 F. Zakeri et al.
  • 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1439957131, Iran
  • 2Photogrammetry and Remote Sensing Department, K. N. Toosi University of Technology, Tehran 1969764499, Iran

Keywords: Curvelet Transform, SAR Images, Speckle Noise, Statistical Modeling

Abstract. Synthetic aperture radar (SAR) images are corrupted by speckles, which influence the interpretation of the images. Therefore, to reduce speckles and obtain reliable information from images, researchers studied different methods. This study proposes a Bayesian multiscale method, to reduce speckles in SAR images. First, it was shown that Laplacian probability density function can capture the characteristics of noise-free curvelet coefficients, and then, a maximum a posteriori (MAP) estimator was designed for estimating them. Comparison of the results obtained with those obtained from conventional speckle filters, such as Lee, Kuan, Frost, and Gamma filters, and also curvelet non-Bayesian despeckling, shows better achievement of the proposed algorithm. For instance, the improvement in different parameters is as follows: ‘noise mean value’ (NMV) 0.24 times, ‘noise standard deviation’ (NSD) 0.34 times, ‘mean square difference’ (MSD) 2.6 times and ‘equivalent number of looks’ (ENL) 0.61 times.