Comparative analysis among radar image filters for flood mapping

Due to the characteristics of microwave signals, Radar satellite image has been used for food detection without weather and time infuence. The more methods of food detection were developed, the more detection rate of food area has been increased. Since food causes a lot of damages, fooded area should be distinguished from non fooded area. Also, the detection of food area should be accurate. Therefore, not only image resolution but also the fltering process is critical to minimize resolution degradation. Although a resolution of radar images become better as technology develops, there were a limited focused on a highly suitable fltering methods for food detection. Thus, the purpose of this study is to find out the most appropriate fltering method for food detection by comparing three fltering methods: Lee flter, Frost flter and NL-means flter. Therefore, to compare the flters to detect foods, each flters are applied to the radar image. Comparison was drawn among fltered images. Then, the food map, results of fltered images are compared in that order. As a result, Frost and NL-means flter are more effective in removing the speckle noise compared to Lee flter. In case of Frost flter, resolution degradation occurred severly during removal of the noise. In case of NL-means flter, shadow effect which could be one of the main reasons that causes false detection were not eliminated comparing to other flters. Nevertheless, result of NL-means flter shows the best detection rate because the number of shadow pixels is relatively low in entire image. Kappa coefficient is scored 0.81 for NL-means filtered image and 0.55, 0.64 and 0.74 follows for non filtered image, Lee filtered image and Frost filtered image respectively. Also, in the process of NL-means flter, speckle noise could be removed without resolution degradation. Accordingly, fooded area could be distinguished effectively from other area in NL-means fltered image.