The Hierarchical Hybrid Fuzzy‐Neural Network Based on Lasso Function and Its Application to Classification of Remote Sensing Images

Abstract

In this paper, a new algorithm for the hierarchical hybrid fuzzy-neural network model is proposed. The Takagi-Sugeno model and triangular membership function are adopted in the fuzzy system, and the Lasso function of the 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 and Anxi in Fujian Province is conducted. Compared with other neural networks, the classification result with the proposed approach is the most accurate, which proves its feasibility and validity, and can be used as a new classification method for surface features on remote sensing images.