基于Lasso函数的分层混合模糊-神经网络及其在遥感影像分类中的应用

本文提出一种新的分层混合模糊-神经网络(HHFNN)算法.在模糊系统中使用Takagi-Sugeno模型和三角波隶属函数.同时,为降低离散输入变量中可能存在的强交互作用,采用了系数收缩机制中的Lasso函数.最后,以福建的漳平洛阳—安溪潘田地区LANDSAT ETM+遥感影像数据地物分类为例,应用本文的改进算法与其他神经网络算法进行分析比较,得到了较高的分类精度,验证了采用基于Lasso函数的T-S型分层混合模糊-神经网络的可行性和有效性,可作为一种新的遥感影像地物分类方法.

HHFNN based on Lasso Function and its application in remote sensing image classification

In this paper, a new algorithm for hierarchical hybrid fuzzy-neural network model is proposed. Takagi-Sugeno model and triangular membership function are adopted in fuzzy system, and lasso function of coefficient contraction method is used to reduce the strong interaction among discrete input variables. In the end, an experimental test on surface features classification by using LANDSAT ETM+ remote sensing image data of Zhangping Luoyang-Anxi Pantian in Fujian Province is conducted. Compared with other neural networks, the classification with the proposed approach is the most accurate, which proves its feasibility and validity, and can be used as a new surface features classification method on remote sensing image.