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Author: search.filters.author.Intajag, SathitSubject: search.filters.subject.Speckle noise

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    SAR segmentation using anisotropic diffusion
    (2008-01-01)
    Intajag, Sathit
    ;
    Tipsuwanporn, Vittaya
    ;
    Cheevasuwit, Fusak
    Speckle effects are commonly observed in synthetic aperture radar (SAR) images. The human eye is capable of deriving meaningful information from SAR images; however, an automatic or semi-automatic processing algorithm has difficulty in distinguishing objects in the images because of noise effects present in those images. This paper presents a segmentation method for SAR images, which employs an anisotropic diffusion algorithm. In the proposed scheme, a SAR image is transformed into a logarithmic domain where the diffusion process is used to grow homogeneous regions in the noise environment until the regions reach some criteria for homogeneity; consequently, the segmented image in the logarithm domain is converted to the intensity domain by an exponential function. To grow homogeneous regions the adaptive diffusion method is introduced with a tensor technique in which tensor data are varied with the neighboring pixels. The diffusion algorithm will stop itself by a standard deviation divided by the mean, which is provided according to the homogeneity criteria. Results are shown on both synthetic and satellite SAR images. The evaluation of the proposed method employs the theoretical gain of equivalent numbers of looks (ENL). © 2008 The Institute of Electrical Engineers of Japan.
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    Speckle noise estimation with generalized gamma distribution
    (2006-12-01)
    Intajag, Sathit
    ;
    Chitwong, Sakreya
    Speckle noise is an inherent property of a synthetic aperture radar (SAR) image, and it generally tends to reduce the image resolution and contrast. The speckle noise estimation is an important prerequisite, whenever SAR image is used for object segmentation. Among the many methods in statistical description that have been proposed to perform the estimation, there exists a class of approaches that use a multiplicative model of speckled image formation, such as Rayleigh distribution, K-distribution, Weibull distribution etc. In this paper, generalized gamma (GG) distribution is used to estimate the noise characteristics. GG distribution is especially attractive because it contains several distributions as special cases, viz. Rayleigh, exponential, Weibull, and log-normal. The major parameter of the GG distribution is estimated according to maximum likelihood (ML) principle. The proposed method works successfully when the solution is located in the parameter space. For verifying the performance of the proposed scheme compared to the other methods, we use a χ<sup>2</sup> goodness-of-fit (GOF) test. © 2006 ICASE.
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    Speckle reduction using fuzzy morphological anisotropic diffusion
    (2005-12-01)
    Easanuruk, Somchai
    ;
    Mitatha, Somsak
    ;
    Intajag, Sathit
    ;
    Chitwong, Sakreya
    One of important tasks of radar image processing is reducing speckle noise as preprocessing to enhance preformance of other processing such as segmentation, classification, etc. In this paper, we then apply the fuzzy morphology together with anisotropic diffusion to reduce speckled noise of SAR image. Anisotropic diffusion is designed based on additive noise model, but the form of speckled image is in multiplicative speckle model. To transform additive noise model into multiplicative speckle model, logarithmic transformation is then used. Our algorithm performs in log-domain. Finally, despeckled image being in log-domain is converted into spatial domain by using exponential transformation. Simulated image as speckled image is performed with our algorithm to show and compare results with recent reports in term of both signal to noise ratio (SNR) and the equivalent number of looks (ENL). Also, real SAR image is performed to confirm results in term of ENL only. Results from our experiment are shown that de-speckled image can smooth out in homogeneous area and preserve edge in heterogeneous area. Both visual image and numerical results are used to show all results. © 2005 IEEE.