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将关联向量机应用于高光谱影像分类,实现高维空间中训练样本不足时分类器的精确建模。从稀疏贝叶斯理论出发,分析关联向量机原理,探讨一对多、一对一和两种直接的多分类方法。实验环节比较了各种多分类方法,并从精度、稀疏性两方面将关联向量机与支持向量机等经典算法比较。实验结果表明,两种直接的多分类方法内存占用大、效率低;一对多精度最高,但效率较低;一对一计算效率最高,精度与一对多近似。关联向量机精度不如支持向量机,但解更稀疏,测试样本较多时实时性好,适合处理大场景高光谱影像的分类问题。 更多还原

【Abstract】 The relevance vector machine(RVM) is used to process the hyperspectral image in this paper to estimate the classifiers precisely in the high dimensional space with limited training samples.The detail of RVM is firstly discussed based on the sparse Bayesian theory.Then four multi-class strategies are analyzed,including One-vs-All(OAA),One-vs-One(OAO) and two direct multi-class strategies.In the experiments,the multi-class strategies are compared and RVM is further compared with several classical ... 更多还原