Investigation of the effect of different SAR Despeckling filters on Radar image classification accuracy.
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The innumerable capabilities of SAR image have contributed to its several applications, topmost of which is land cover classification. The presence of speckle in SAR images makes the automated digital image classification a challenging task. Reduction of speckles noise is therefore a very important component in SAR image processing. This research therefore aimed at investigating the effect of Gamma Map, Median, Mean and Lee de-speckling filters on the classification accuracy of land cover maps. Secondary data source i.e., dual polarized (VV+VH), high resolution, ground range detected (GRD) Sentinel 1A image of Kampala district was acquired and used. The image was filtered using the aforementioned de-speckling filters while varying the filter window size and later subjected to unsupervised classification using K-Mean clustering algorithm. The results were evaluated using quantitative measures, which included Equivalent Number of Look (ENL), Speckle suppression index (SSI), Speckle suppression and mean preservation index (SMPI), Mean square error (MSE), Producer classification accuracy, User classification accuracy, Overall classification accuracy and Kappa coefficient. It was observed that the aforementioned quantitative evaluation measures cannot achieve the best performance in speckle suppression and feature preservation simultaneously. De-speckle filters with good noise removal capabilities often tended to degrade the spatial and radiometric resolution of the original image thus leads to poor classification accuracy. The classification accuracy of landcover maps produced by SAR imagery is dependent on the quality of the image which can be attributed to the different de-speckling techniques and filter window sizes used. The results of the research showed that Gamma Map Lee and Mean filter provide a good balance in feature preservation as well as speckle reduction compared to Median filter which has a significant performance in speckle reduction, but at the cost of feature loss. An overall classification accuracy of 89.6 %, with 0.87 kappa coefficient value was archived using Gamma Map filter. It was also observed that majority of the de-speckling filters (Gamma Map, Median and Lee filter) attained best overall classification accuracies and Kappa Coefficient values with 5x5 kernel size, except for Mean filter. Thus, concludes 5x5 filter window size and Gamma Map filter to be the best for land cover classification due to the ability to remove noise and preserve the details of the image.