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Local Contrast Based Adaptive SAR Speckle Filter

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Abstract

In this paper, we propose an adaptive filtering technique for Synthetic Aperture Radar (SAR) images. A new windowing technique is introduced where the total window is divided into five equal sized overlapping sub-windows. The pixel to be filtered is a part of each of these sub-windows. A weighted mean of all sub-windows is computed for the pixel under consideration. The weights are accounted from a measure of heterogeneity calculated for each sub-windows. The filter is able to adapt automatically and adjust the speckle suppression strength based on local statistics. This allows the filter to preserve edges while strongly suppressing speckle over homogeneous areas. The proposed filter was compared with some well known SAR filtering techniques in terms of speckle suppression and edge preservation ability. Several experiments were performed on datasets acquired from both air-borne and space-borne SAR platforms. Some well known indices were used for quantitative comparison with other filters. Among the filters compared, the proposed filter shows good speckle suppression ability while still exhibiting reasonable edge preservation ability.

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Acknowledgments

The authors would like to thank NASA/JPL-Caltech for providing sample imagery of UAVSAR and AIRSAR used in this work. TerraSAR-X sample dataset is courtesy of Astrium GEO-Information Services.

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Authors and Affiliations

  1. Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India

    Sanjay Shitole, Shaunak De, Avik Bhattacharya, Y. S. Rao & B. Krishna Mohan

  2. Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India

    Mayank Sharma

Authors
  1. Sanjay Shitole
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  2. Mayank Sharma
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  3. Shaunak De
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Corresponding author

Correspondence to Sanjay Shitole.

Appendix

Appendix

Algorithm for the proposed filter is presented here.

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Shitole, S., Sharma, M., De, S. et al. Local Contrast Based Adaptive SAR Speckle Filter. J Indian Soc Remote Sens 45, 451–462 (2017). https://doi.org/10.1007/s12524-016-0613-2

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