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Automatic computation of speckle standard deviation in SAR images

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Being a coherent reception system, Synthetic Aperture Radar (SAR) sensors are highly liable to speckle noise effect, which masks details and patterns in the image, and therefore, degrades interpretation. Speckling may be reduced by applying filtering techniques to SAR multilook images. The major problem that arises from this type of method is the estimation of input parameters: sliding window size and speckle standard deviation. The present paper describes a manual and two automatic methods devised to estimate speckle standard deviation based on the texture concept, in order to extract homogenous regions. The automatic method were specially developed to improve results obtained with the manual one, and the so-called least-squares approach and mean approach were considered. The mean approach was introduced as an alternative to the least-squares approach. It performs better in terms of computing time and disk space use, and even shows a slightly higher accuracy when tested against artificially speckled images. Manual and automatic methods were applied as an example using ERS-1/SAR one-look and three-look images with different features, obtained over several Austral and Antartic regions of Argentina. Results show that the automatic method is a valuable tool for estimating speckle standard deviation, being accurate, less tedious, and preventing typical human errors associated with manual tasks.
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Document Type: Research Article

Affiliations: Satellite Radiometry Division-COCICET, IAFE C.C. 67 Suc 28 (1428), Buenos Aires, Argentina

Publication date: 2000-10-15

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