Speckle filtering for SAR imagery learning through heuristic method

2011 ◽  
Author(s):  
David Mata-Moya ◽  
Pilar Jarabo-Amores ◽  
Jose M. Munoz-Ferreras ◽  
Jaime Martin de Nicolas-Presa Angel ◽  
Palma Vazquez
Keyword(s):  
Heuristic Method ◽  
Sar Imagery ◽  
Speckle Filtering

On finding optimal speckle filtering for extraction of vegetation biophysical information using Sentinel-1 SAR imagery

2021 ◽  
Author(s):  
Syamani D. Ali ◽  
Abdi Fithria ◽  
Adi Rahmadi ◽  
Arfa A. Rezekiah
Keyword(s):  
Sar Imagery ◽  
Speckle Filtering

Improved Sigma Filter for Speckle Filtering of SAR Imagery

2009 ◽  
Vol 47 (1) ◽  
pp. 202-213 ◽  
Author(s):  
Jong-Sen Lee ◽  
Jong-Sen Lee ◽  
Jen-Hung Wen ◽  
T.L. Ainsworth ◽  
Kun-Shan Chen ◽  
...  
Keyword(s):  
Sar Imagery ◽  
Speckle Filtering

scholarly journals Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine

2021 ◽  
Vol 13 (10) ◽  
pp. 1954
Author(s):  
Adugna Mullissa ◽  
Andreas Vollrath ◽  
Christelle Odongo-Braun ◽  
Bart Slagter ◽  
Johannes Balling ◽  
...  
Keyword(s):  
Open Data ◽  
Google Earth ◽  
Technical Note ◽  
Data Preparation ◽  
Large Area ◽  
Wide Range ◽  
Google Earth Engine ◽  
Sar Imagery ◽  
Noise Correction ◽  
Speckle Filtering

Sentinel-1 satellites provide temporally dense and high spatial resolution synthetic aperture radar (SAR) imagery. The open data policy and global coverage of Sentinel-1 make it a valuable data source for a wide range of SAR-based applications. In this regard, the Google Earth Engine is a key platform for large area analysis with preprocessed Sentinel-1 backscatter images available within a few days after acquisition. To preserve the information content and user freedom, some preprocessing steps (e.g., speckle filtering) are not applied on the ingested Sentinel-1 imagery as they can vary by application. In this technical note, we present a framework for preparing Sentinel-1 SAR backscatter Analysis-Ready-Data in the Google Earth Engine that combines existing and new Google Earth Engine implementations for additional border noise correction, speckle filtering and radiometric terrain normalization. The proposed framework can be used to generate Sentinel-1 Analysis-Ready-Data suitable for a wide range of land and inland water applications. The Analysis Ready Data preparation framework is implemented in the Google Earth Engine JavaScript and Python APIs.

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