Extraction of Auroral Oval Regions Using Suppressed Fuzzy C Means Clustering

Abstract

Based on the fuzzy suppressed c-means clustering algorithm, a new method is developed for extracting auroral oval regions from images acquired by the Ultraviolet Imager aboard the POLAR satellite. Compared with different variations of fuzzy c-means clustering methods, suppressed fuzzy c-means clustering was proposed with the intention of improving convergence rate by modifying membership values, which is more suitable for studying auroral behavior over time with considering a series of images. However, traditional suppressed c-means clustering algorithms employ the same suppressed parameter for modifying fuzzy membership degrees of all pixels, ignoring the fact that image characteristics varies from one auroral oval images to another. In this paper, the technique parameters which is set beforehand will be automatically selected according to the intrinsic characteristic of each auroral oval image. Moreover, corresponding operations are devised for modifying membership values of different pixels according to their real needs, which makes it clear to decide whether to proceed with further determination or just make decision on the basis of already obtained analysis results. Experimental results on auroral oval images acquired from an online database collected by NASA Polar satellite's Ultraviolet Imager indicate that the proposed method extracts more accurate auroral oval regions than traditional suppressed c-means clustering method in most cases.