Improved Sigma Filter for Speckle Filtering of SAR Imagery
The Lee sigma filter was developed in 1983 based on the simple concept of two-sigma probability, and it was reasonably effective in speckle filtering. However, deficiencies were discovered in producing biased estimation and in blurring and depressing strong reflected targets. The advancement of synt...
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| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 2009-01, Vol.47 (1), p.202-213 |
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| creator | Jong-Sen Lee Jen-Hung Wen Ainsworth, T.L. Kun-Shan Chen Chen, A.J. |
| description | The Lee sigma filter was developed in 1983 based on the simple concept of two-sigma probability, and it was reasonably effective in speckle filtering. However, deficiencies were discovered in producing biased estimation and in blurring and depressing strong reflected targets. The advancement of synthetic aperture radar (SAR) technology with high-resolution data of large dimensions demands better and efficient speckle filtering algorithms. In this paper, we extend and improve the Lee sigma filter by eliminating these deficiencies. The bias problem is solved by redefining the sigma range based on the speckle probability density functions. To mitigate the problems of blurring and depressing strong reflective scatterers, a target signature preservation technique is developed. In addition, we incorporate the minimum-mean-square-error estimator for adaptive speckle reduction. Simulated SAR data are used to quantitatively evaluate the characteristics of this improved sigma filter and to validate its effectiveness. The proposed algorithm is applied to spaceborne and airborne SAR data to demonstrate its overall speckle filtering characteristics as compared with other algorithms. This improved sigma filter remains simple in concept and is computationally efficient but without the deficiencies of the original Lee sigma filter. |
| doi_str_mv | 10.1109/TGRS.2008.2002881 |
| format | Article |
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However, deficiencies were discovered in producing biased estimation and in blurring and depressing strong reflected targets. The advancement of synthetic aperture radar (SAR) technology with high-resolution data of large dimensions demands better and efficient speckle filtering algorithms. In this paper, we extend and improve the Lee sigma filter by eliminating these deficiencies. The bias problem is solved by redefining the sigma range based on the speckle probability density functions. To mitigate the problems of blurring and depressing strong reflective scatterers, a target signature preservation technique is developed. In addition, we incorporate the minimum-mean-square-error estimator for adaptive speckle reduction. Simulated SAR data are used to quantitatively evaluate the characteristics of this improved sigma filter and to validate its effectiveness. The proposed algorithm is applied to spaceborne and airborne SAR data to demonstrate its overall speckle filtering characteristics as compared with other algorithms. This improved sigma filter remains simple in concept and is computationally efficient but without the deficiencies of the original Lee sigma filter.</description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2008.2002881</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithms ; Applied geophysics ; Blurring ; Computational efficiency ; Computer simulation ; Earth sciences ; Earth, ocean, space ; Estimators ; Exact sciences and technology ; Filtering ; Filtering algorithms ; Filters ; Filtration ; Internal geophysics ; Laboratories ; Probability density function ; Radar scattering ; Remote sensing ; Sigma filter ; Spaceborne radar ; Speckle ; speckle filtering ; Synthetic aperture radar ; synthetic aperture radar (SAR)</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2009-01, Vol.47 (1), p.202-213</ispartof><rights>2009 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The proposed algorithm is applied to spaceborne and airborne SAR data to demonstrate its overall speckle filtering characteristics as compared with other algorithms. 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However, deficiencies were discovered in producing biased estimation and in blurring and depressing strong reflected targets. The advancement of synthetic aperture radar (SAR) technology with high-resolution data of large dimensions demands better and efficient speckle filtering algorithms. In this paper, we extend and improve the Lee sigma filter by eliminating these deficiencies. The bias problem is solved by redefining the sigma range based on the speckle probability density functions. To mitigate the problems of blurring and depressing strong reflective scatterers, a target signature preservation technique is developed. In addition, we incorporate the minimum-mean-square-error estimator for adaptive speckle reduction. Simulated SAR data are used to quantitatively evaluate the characteristics of this improved sigma filter and to validate its effectiveness. The proposed algorithm is applied to spaceborne and airborne SAR data to demonstrate its overall speckle filtering characteristics as compared with other algorithms. This improved sigma filter remains simple in concept and is computationally efficient but without the deficiencies of the original Lee sigma filter.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TGRS.2008.2002881</doi><tpages>12</tpages></addata></record> |
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| subjects | Algorithms Applied geophysics Blurring Computational efficiency Computer simulation Earth sciences Earth, ocean, space Estimators Exact sciences and technology Filtering Filtering algorithms Filters Filtration Internal geophysics Laboratories Probability density function Radar scattering Remote sensing Sigma filter Spaceborne radar Speckle speckle filtering Synthetic aperture radar synthetic aperture radar (SAR) |
| title | Improved Sigma Filter for Speckle Filtering of SAR Imagery |
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