李林宜, 李德仁. PSO-RBF应用于航空和卫星遥感影像的纹理分类[J]. 武汉大学学报 ( 信息科学版), 2009, 34(9): 1051-1054.
引用本文: 李林宜, 李德仁. PSO-RBF应用于航空和卫星遥感影像的纹理分类[J]. 武汉大学学报 ( 信息科学版), 2009, 34(9): 1051-1054.
shu
LI Linyi, LI Deren. Applied PSO-RBF to Aerial and Satellite Remote Sensing Image Texture Classification[J]. Geomatics and Information Science of Wuhan University, 2009, 34(9): 1051-1054.
Citation: LI Linyi, LI Deren. Applied PSO-RBF to Aerial and Satellite Remote Sensing Image Texture Classification[J]. Geomatics and Information Science of Wuhan University, 2009, 34(9): 1051-1054.
shu

PSO-RBF应用于航空和卫星遥感影像的纹理分类

Applied PSO-RBF to Aerial and Satellite Remote Sensing Image Texture Classification

    摘要
  • 摘要: 粒子群优化算法(PSO)是基于群体智能的新型进化计算技术,将核函数参数选取问题转换为优化问题,用PSO来进行处理,并将PSO与RBF联合(PSO-RBF)应用于航空和卫星遥感影像的纹理分类,实验结果验证了此方法的有效性。

     

    Abstract: Particle swarm optimization(PSO) is a new evolutionary computing technique which is based on swarm intelligence.Kernel function parameter selection problem is transformed into the optimization problem and PSO is used to get the optimal kernel function parameters.The combined algorithm of PSO and RBF(PSO-RBF) is applied to aerial and satellite remote sensing image texture classification.The experimental results show that the proposed method is effective.

     

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