Evaluation of SAR speckle filter technique for inundation mapping

Abstract

The presence of speckle in visual images makes the automated digital image classification a challenging task. Therefore, reduction of speckles is an important pre-processing step. The choice of speckle filter depends on the requirements of an application and the characteristics of the dataset. In this study, some most preferred speckle filters are assessed for the data from Sentinel-1 to map flood extent. The Sentinel-1 (VV-vertical transmit, vertical receive and VH- vertical transmit, horizontal receive) polarizing filter data were used to evaluate machine learning algorithms, namely, random forest (RF) and support vector machine (SVM), to classify an inundated area. The accuracies of the classifications were assessed by kappa coefficient, overall accuracies, and producer's and user's accuracies. The present study suggests an approach to monitor damage and provide basic information to help local communities manage water-related risk, land planning, water management, and flood control programs.