基于鱼群算法的极限学习机影像分类方法优化

在传统极限学习机(ELM)研究的基础上,考虑到传统ELM参数的不确定会导致整体分类精度下降,利用仿生鱼群算法(AF)对ELM的小波核参数和正则化参数进行寻优,并构造参数优化后的小波ELM影像分类模型(AF-ELM)。通过实验比较了该算法与人工神经网路(ANN)、支持向量机(SVM)、极限学习机(ELM)等标准分类器在遥感影像分类上的精度与速度差异,并且与ELM多项式核、RBF核分类算法进行比较分析,验证了AF-ELM在分类速度和精度上的优越性。实验结果表明,AF-ELM分类方法分类速度较快,精度较高,均优于其他分类方法。能较好地应用于遥感影像上各类地物要素的自动提取。

Optimization of ELM Classification Model for Remote Sensing Image Based on Artificial Fish-swarm Algorithm

As a new means of earth resource survey, land use change and coverage (LUCC) and ecological environment monitoring, remote sensing technology has a great advantage. The automatic classification for remote sensing image is the key technology to extract rich ground-object information and monitor the dynamic change of LUCC. Machine learning can flexibly build a model portrayed by parameters, and automatically extract information, which has been widely used in image classification because of its good robustness and convergence, and easy to be combined with other methods. Based on the study of traditional extreme learning machine (ELM) theory, the optimal selection of kernel function parameters and regularizing parameters were performed by using artificial fish swarm algorithm (AF) and the optimal ELM image classification model (AF-ELM) was constructed. The classification model used AF to optimize the wavelet kernel parameters and regularizing parameters of ELM to improve the classification accuracy. After that the classification for multi-spectral remote sensing image was implemented by using the parameter-optimized ELM classifier, meanwhile, compared with some standard classifier such as artificial neural networks(ANM), support vector machine (SVM) and extreme learning machine (ELM), and it was comparatively analyzed with the ELM polynomial kernel and RBF kernel classification algorithm. The experiments proved that optimal AF-ELM classifier was more faster and accurate, which was superior to those before-mentioned classifiers. It can be used for the automatic extraction of various elements from remote sensing image.