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- 2017
面向卫星云图云分类的自适应模糊支持向量机
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Abstract:
云类识别是实现卫星云图自动分析的基础，针对卫星云图易受噪声干扰且不同云系往往相互交叠的特点，构造一种面向云类识别的自适应模糊支持向量机。该方法不仅改进了隶属度函数的表现形式，而且通过定义控制临界隶属度和隶属度衰减趋势的参数，使隶属度能根据不同云系样本的具体分布特性自适应调整，解决了传统模糊支持向量机的隶属度函数难以反映样本分布的问题。在MTSAT卫星云图上的实验结果表明，通过提取云图可见光通道的反照率、红外通道的亮温及三种亮温差作为云图的光谱特征，并结合统计纹理特征，所构造的自适应模糊支持向量机分类器能有效区分晴空区、低云、中云、高云及直展云；云类识别准确率优于标准支持向量机和传统模糊支持向量机，且具有更强的稳定性和自适应性
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