为降低高光谱影像的数据维数,提高地物分类识别效率,提出了一种地物分类方法——核直接线性判别分析(Kernel Direct Linear Discriminant Analysis,KDLDA)子空间法;并推导出类先验概率的一般形式下KDLDA的解。KDLDA子空间法先采用KDLDA提取遥感影像的非线性可分特征,然后在KDLDA子空间采用最小距离分类器进行分类识别。机载可见光/红外成像光谱仪(Airborne Visible/Infrared Imaging Spectrometer,AVIRIS)的高光谱影像识别结果表明,相比原空间法、LDA子空间法、直接线性判别分析(Direct Linear Discriminant Analysis,DLDA)子空间法、核线性判别分析(Kernel Linear Discriminant Analysis,KLDA)子空间法,KDLDA子空间法可显著提高识别效率。
An evolutionary network driven by dynamics is studied and applied to the graph coloring problem.From an initial structure,both the topology and the coupling weights evolve according to the dynamics.On the other hand,the dynamics of the network are determined by the topology and the coupling weights,so an interesting structure-dynamics co-evolutionary scheme appears.By providing two evolutionary strategies,a network described by the complement of a graph will evolve into several clusters of nodes according to their dynamics.The nodes in each cluster can be assigned the same color and nodes in different clusters assigned different colors.In this way,a co-evolution phenomenon is applied to the graph coloring problem.The proposed scheme is tested on several benchmark graphs for graph coloring.
