Improved Sigma Filter for Speckle Filtering of SAR Imagery

By Jong-Sen Lee, Jen-Hung Wen, Thomas L. Ainsworth, Kun Shan Chen, A. J. Chen. IEEE Transactions on Geoscience and Remote Sensing, 2009

601 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geophysics

View Paper: https://www.semanticscholar.org/paper/30910d39c517fb82a5ca080a6a8aba0faa05aae7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Marc Jäger
2. Florence Tupin
3. Charles-Alban Deledalle
4. Andreas Reigber
5. Loïc Denis

In Computer Vision and Pattern Recognition

1. Tiziano Bianchi
2. Alessandro Lapini
3. Fabrizio Argenti
4. Luciano Alparone
5. Marc Jäger
6. Florence Tupin
7. Charles-Alban Deledalle
8. Andreas Reigber

Citations in the corpus

• Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine (2021, methodology): “These speckle filters range from a simple blind low-pass filter, such as the boxcar filter, to different adaptive filters, such as the Lee filter [13], Gamma Maximum A-posterior (MAP) Filter [14,15], Refined Lee filter [16], and the improved Lee sigma filter [17].”
• A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images (2013, background): “Eventually, in [104] the bias problem is solved by redefining the sigma range based on the speckle pdf.”
• NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising (2015, methodology): “selection bias, which can be corrected with a sigma-range preselection, following the idea of the improved sigma filter [26].”