OTB.Despeckle: Perform speckle noise reduction on SAR image.
SAR images are affected by speckle noise that inherently exists in and which degrades the image quality. It is caused by the coherent nature of back-scattered waves from multiple distributed targets. It is locally strong and it increases the mean Grey level of a local area. Reducing the speckle noise enhances radiometric resolution but tend to decrease the spatial resolution.Several different methods are used to eliminate speckle noise, based upon different mathematical models of the phenomenon. The application includes four methods: Lee [1], Frost [2], GammaMAP [3] and Kuan [4]. We sum up below the basic principle of this four methods: * Lee : Estimate the signal by mean square error minimization (MMSE) on a sliding window. * Frost : Also derived from the MMSE criteria with a weighted sum of the values within the window. The weighting factors decrease with distance from the pixel of interest. * GammaMAP : Derived under the assumption of the image follows a Gamma distribution. * Kuan : Also derived from the MMSE criteria under the assumption of non stationary mean and variance. It is quite similar to Lee filter in form.
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{"id": "OTB.Despeckle", "title": "Perform speckle noise reduction on SAR image.", "description": "SAR images are affected by speckle noise that inherently exists in and which degrades the image quality. It is caused by the coherent nature of back-scattered waves from multiple distributed targets. It is locally strong and it increases the mean Grey level of a local area. Reducing the speckle noise enhances radiometric resolution but tend to decrease the spatial resolution.Several different methods are used to eliminate speckle noise, based upon different mathematical models of the phenomenon. The application includes four methods: Lee [1], Frost [2], GammaMAP [3] and Kuan [4]. We sum up below the basic principle of this four methods: * Lee : Estimate the signal by mean square error minimization (MMSE) on a sliding window. * Frost : Also derived from the MMSE criteria with a weighted sum of the values within the window. The weighting factors decrease with distance from the pixel of interest. * GammaMAP : Derived under the assumption of the image follows a Gamma distribution. * Kuan : Also derived from the MMSE criteria under the assumption of non stationary mean and variance. 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Last modified: Sat Dec 4 00:09:36 CET 2021