【摘要】 极化SAR影像高维数以及高分辨率带来的大数据量特点使得影像分类的复杂度不断增加。粒子群优化(PSO)算法作为新型进化计算技术,具有强大的全局寻优能力。本文研究了一种基于PSO算法的极化SAR影像的分类方法。该方法首先利用H/α方法对数据进行基于散射机理的初分类;然后利用分类结果对PSO算法进行初始化;最后采用PSO对极化SAR数据迭代分类。实验采用NASA-JPL实验室的极化SAR数据以及中国电子科技集团X波段原型样机的高分辨率数据。结果表明,H/α-PSO分类方法较H/α-Wishart分类精度及目视效果均有所提高。更多还原

【Abstract】 The high dimensionality of Polarimetric SAR image and the large volume which is resulted by the high resolution make the complexity of PolSAR image classification increasing. As a new evolutionary computation algorithm, Particle swarm optimization( PSO) has powerful ability of making a global optimization. A classification method of PolSAR image based on PSO algorithm is studied in this paper. Firstly, an initial classification based on scattering mechanism is done by taking use of the H / α method in the method. And then this classification result is used to initialize PSO algorithm. Finally the PSO is applied to output classification result of PolSAR data iteratively. The L-band PolSAR image of NASA-JPL laboratory and X-band high resolution PolSAR image of China Electronic Technology Group were applied in the classification experiments. And the result shows that the classification precision of the PSO-based algorithm is higher than the H / α-Wishart algorithm.更多还原