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Proceedings Paper

Multiscale methods applied to the analysis of SAR images
Author(s): Albert Bijaoui; Yanling Fang; Yves Bobichon; Frederic Rue
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Paper Abstract

The analysis of SAR images requires in a first step to reduce the speckle noise which is due to the coherent character of the RADAR signal. The application of the minimum variance bound estimator leads to process the energy image instead of the amplitude one for the reduction of this multiplicative noise. The proposed analyzing methods are based on a multiscale vision model for which the image is only described by its significant structural features at a set of dyadic scales. The multiscale analysis is performed by a redundant discrete wavelet transform, the a trous algorithm. The filtering algorithm is interactive. At each step we compute the ratio between the observed energy image and the restored one. We detect at each scale the significant structures, by taking into account the exponential probability distribution function of the energy for the determination of the significant wavelet coefficients. The ratio is restored from its significant coefficients, and the restored image is updated. The iterations are stopped when any significant structure is detected in the ratio. Then, we are interested to extract and to analyze the contained objects. The multiscale analysis allows us an approach well adapted to diffused objects, without contrasted edges. An object is defined as a local maximum in the wavelet transform space (WTS). All the structures form a 3D connected set which is hierarchically organized. This set gives the description of an object in the WTS. The image of each object is restored y an inverse algorithm. The comparison between images taken at different epochs is done using the multiscale vision model. THat allows us to enhance the features at a given scale which have significantly varied. The correlation coefficients between the structures detected at each scale are far form the ones obtained between the pixel energy. For example, this method is very suitable to detect and to describe faint large scale variations.

Paper Details

Date Published: 8 October 1996
PDF: 8 pages
Proc. SPIE 2823, Statistical and Stochastic Methods for Image Processing, (8 October 1996); doi: 10.1117/12.253449
Show Author Affiliations
Albert Bijaoui, Observatoire de la Cote d'Azur (France)
Yanling Fang, Observatoire de la Cote d'Azur (France)
Yves Bobichon, Observatoire de la Cote d'Azur (France)
Frederic Rue, Observatoire de la Cote d'Azur (France)


Published in SPIE Proceedings Vol. 2823:
Statistical and Stochastic Methods for Image Processing
Edward R. Dougherty; Francoise J. Preteux; Jennifer L. Davidson, Editor(s)

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