Tsallis entropy and particle swarm optimization-based cyclone image vortex localization

Abstract

Cyclone vortex localization under varying conditions of saturated spiral bands is challenging. This paper presents a unique combination of image processing techniques, viz., Sequential Cross-Correlation (SCC) and Multi-Level Thresholding (MLT) for vortex localization. SCC is used for cyclone detection in a full-disk satellite imagery, and is based on the high degree of correlation in the sequence of cyclone stages. MLT is used for vortex localization in the detected Tropical Cyclone (TC), and is based on Tsallis entropy and particle swarm optimization (PSO). These consider unimodal distribution of the pixel intensity and non-extensive nature of cyclone for image segmentation. The vortex co-ordinates thus obtained will be the authentic estimate of the TC's vortex and is further used for the TC tracking. The proposed algorithm is applied on the full-disk visible and infrared (IR) imagery of size 744 × 676 obtained from Geostationary Operational Environmental Satellites, namely GOES-12 and 13 and the experimental results indicate that proposed algorithm efficiently tracks the TC with the best average Euclidean distance error of 23 per TC.