为解决 SVM在积雨云检测中的难题, 本文构造了一种模糊支持向量机( FSVM), 首先根据训练样本的分布特性, 定义了相邻样本距离类中心的距离变化率, 然后通过计算距离变化率来剔除训练集中可能的噪声与野值样本, 从而有效克服了传统基于紧密度的 FSVM在计算最小超球半径时易受噪声与野值干扰的缺点, 使得所计算的隶属度能更好地反映不同样本的差异。实验结果表明, 对于 FY2D卫星云图, 采用从不同通道所提取的光谱特征, 本文方法的积雨云检测准确率与传统 SVM和基于紧密度的 FSVM相比, 分别平均提高 2%和 1%, 且具有更强的适应性及噪声鲁棒性。

Using satellite imagery for cumulus cloud detection has an important significance for preventing meteorological disasters. Support Vector Machine (SVM), which can seek the best compromise between the complexity of the model and the learning ability based on finite sample information, is expected to play a role in the cumulus cloud detection. However, the traditional SVM is very sensitive to the samples of noise and outlier, and doesn’t possess the skill of fuzzy treatment, which doesn’t meet the fuzzy and uneven characteristics of satellite imagery and the complex and diverse cloud patterns. In order to solve the problem of SVM, this paper introduces Fuzzy Support Vector Machine (FSVM) and defines the range-rate of the distances from the adjacent samples to the class center, based on the distribution characteristics of training samples. Then, on the basis of the range-rate, we weed out the possible noises and outliers of training set and overcome the shortcoming that the affinity FSVM is susceptible to noises and outliers at the time of calculating the radius of smallest hyper-sphere, so as to make the obtained membership better reflect the variance of different sample sets. The experimental results show that, for FY2D satellite imageries, extracting 8-d spectral features from different channels, compared with traditional SVM and affinity FSVM, the accuracies of cumulus cloud detection based on the proposed method increase respectively by about 2% and 1%. The proposed method owns stronger adaptability and noise robustness, and can make better effect on early warning disastrous weather such as thunderstorm.