Automated detection of contaminated radar image pixels in mountain areas

Abstract

In mountain areas, radar observations are often contaminated (1) by echoes from high-speed moving vehicles and (2) by point-wise ground clutter under either normal propagation (NP) or anomalous propagation (AP) conditions. Level II data are collected from KMTX (Salt Lake City, Utah) radar to analyze these two types of contamination in the mountain area around the Great Salt Lake. Human experts provide the “ground truth” for possible contamination of either type on each individual pixel. Common features are then extracted for contaminated pixels of each type. For example, pixels contaminated by echoes from high-speed moving vehicles are characterized by large radial velocity and spectrum width. Echoes from a moving train tend to have larger velocity and reflectivity but smaller spectrum width than those from moving vehicles on highways. These contaminated pixels are only seen in areas of large terrain gradient (in the radial direction along the radar beam). The same is true for the second type of contamination-point-wise ground clutters. Six quality control (QC) parameters are selected to quantify the extracted features. Histograms are computed for each QC parameter and grouped for contaminated pixels of each type and also for non-contaminated pixels. Based on the computed histograms, a fuzzy logical algorithm is developed for automated detection of contaminated pixels. The algorithm is tested with KMTX radar data under different (clear and rainy) weather conditions.