Abstract
Abstract. In recent years, the use of Polarimetric Synthetic Aperture Radar (PolSAR) data in different applications dramatically has been increased. In SAR imagery an interference phenomenon with random behavior exists which is called speckle noise. The interpretation of data encounters some troubles due to the presence of speckle which can be considered as a multiplicative noise affecting all coherent imaging systems. Indeed, speckle degrade radiometric resolution of PolSAR images, therefore it is needful to perform speckle filtering on the SAR data type. Markov Random Field (MRF) has proven to be a powerful method for drawing out eliciting contextual information from remotely sensed images. In the present paper, a probability density function (PDF), which is fitted well with the PolSAR data based on the goodness-of-fit test, is first obtained for the pixel-wise analysis. Then the contextual smoothing is achieved with the MRF method. This novel speckle reduction method combines an advanced statistical distribution with spatial contextual information for PolSAR data. These two parts of information are combined based on weighted summation of pixel-wise and contextual models. This approach not only preserves edge information in the images, but also improves signal-to-noise ratio of the results. The method maintains the mean value of original signal in the homogenous areas and preserves the edges of features in the heterogeneous regions. Experiments on real medium resolution ALOS data from Tehran, and also high resolution full polarimetric SAR data over the Oberpfaffenhofen test-site in Germany, demonstrate the effectiveness of the algorithm compared with well-known despeckling methods.
Highlights
Polarimetric analysis enhances the discrimination capability of SAR sensors and this makes the Polarimetric Synthetic Aperture Radar (PolSAR) data very useful for various land use applications
Speckle degrades radiometric resolution of SAR images (Espinoza Molina, Gleich, and Datcu 2012) and understanding PolSAR speckle statistics can be beneficial for different applications such as change detection (Moser and Serpico 2006), ice monitoring (Dierking and Busche 2006), and land cover classification (Tison et al 2004)
The performance of the proposed algorithm for speckle reduction is evaluated in term of quantitative metrics such as signal to noise ratio (SNR), equivalent number of look (ENL), root mean square error (RMSE)
Summary
Polarimetric analysis enhances the discrimination capability of SAR sensors and this makes the PolSAR data very useful for various land use applications. The presence of speckle in PolSAR data complicates the image processing and interpretation and reduces the accuracy of image segmentation and classification Reduction of such noises is a principal step in preprocessing procedure and should be realized before other analysis applied to data. The potential of MRF models to retrieve spatial contextual information makes it desired to reduce the speckle noise of the PolSAR data. This research presents a novel approach for speckle reduction of PolSAR images by combining advanced statistical modeling and spatial context within an MRF framework. A new idea which is proposed for speckle reduction of SAR data based upon weighted summation of these two sections (i.e. pixel-wise and contextual analysis). Our proposed methodology is applied to full polarimetric L medium resolution ALOS data from Tehran, Iran and to high resolution L-band PolSAR data over the Oberpfaffenhofen test-site in Germany These images cover both urban and non-urban areas. Experimental results presented and discussion and conclusion offered in continuation
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