Chitwong, Sakreya

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Chitwong, Sakreya
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Chitwong, S.
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sakreya.ch@kmitl.ac.th

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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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    Automatic SAR segmentation by fuzzy hit-or-miss and homogeneity index
    (2005-12-01)
    Intajag, Sathit
    ;
    Tipsuwanporn, Vittaya
    ;
    Chitwong, Sakreya
    ;
    Suesut, Taweepon
    ;
    Chevasuwit, Fusak
    Object-based segmentation is the first essential step for image processing applications. Recently, segmentation techniques have been developed, but not enough to preserve the significant information contained in the small regions of an image. The proposed method is to partition an image into homogeneous regions by using a fuzzy hitor- miss operator with an inherent spatial transformation, which endows to preserve the small regions. In the proposed scheme, an iterative segmentation technique is formulated as consequential processes. Then, each time in iterating, hypothesis testing is used to evaluate the quality of the segmented regions with a homogeneity index. Our method is unsupervised and uses few parameters, most of which can be calculated from input data. This comparative study indicates that the new iterative segmentation algorithm provides acceptable results as seen in the tested examples of synthetic aperture radar (SAR) images. © 2005 by the American Society for Photogrammetry and Remote Sensing.
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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.