Convective clouds detection in satellite cloud image using fast fuzzy support vector machine

Abstract

Support vector machine (SVM) is easily affected by noises and outliers, and its training time dramatically increases with the growing in number of training samples. Satellite cloud image may easily be deteriorated by noises and intensity non-uniformity with a huge amount of data needs to be processed regularly, so it is hard to detect convective clouds in satellite image using traditional SVM. To deal with this problem, a novel method for detection of convective clouds was proposed based on fast fuzzy support vector machine (FFSVM). FFSVM was constructed by eliminating feeble samples and designing new membership function as two aspects. Firstly, according to the distribution characteristics of fuzzy inseparable sample set and the fact that the classification hyper-plane is only determined by support vectors, this paper uses SVDD, Gaussian model and border vector extraction model comprehensively to design a sample selection method in three steps, which can eliminate most of redundant samples and keep possible support vectors. Then, by defining adaptive parameters related to attenuation rate and critical membership on the basis of the distribution characteristics of training set, an adaptive membership function is designed. Finally, the FFSVM is trained by the remaining samples using adaptive membership function to detect convective clouds. The experiments on FY-2D satellite images show that the proposed method, compared with traditional FSVM, not only remarkably reduces training time, but also further improves the accuracy of convective clouds detection.