Enhancing Echo Processing Through the Integration of Support Vector Machine and Weber’s Law Descriptors

Abstract

Removing ground echoes from weather radar images is a topic of great importance due to their significant impact on the accuracy of processed data. To address this challenge, we aim to develop methods that effectively eliminate ground echoes while preserving the precipitation, which is a crucial meteorological parameter. To accomplish this, we propose to test Local Descriptors based on Weber’s law (WLD), as well as descriptors that combine Weber’s law with Local Binary Pattern (WLBP), using Support Vector Machine (SVM) classifiers to automate the recognition of both types of echoes. The proposed methods are rigorously tested at the sites of Setif and Bordeaux to evaluate their effectiveness in accurately identifying the ground echoes and precipitation. The results of our experiments demonstrate that the proposed techniques are highly effective in eliminating ground echoes while preserving the precipitation, and can be considered satisfactory for practical applications in meteorological data processing.