Satellite Image Classification Using Cascaded Architecture of Neural Fuzzy Network

Abstract

Because satellite images usually contain many complex factors and mix-up samples, a high recognition rate is not easy to attain. Especially for a nonhomogeneous region, the gray values of its satellite image vary greatly, and thus the direct use of gray values cannot do the categorization task correctly. Classification of terrain cover using polarimetric radar is an area of considerable current interest and research. Without the benefit of satellite, we cannot analyze the information of the distribution of soils and cities for a land development, as well as the variation of clouds and volcano for weather forecasting and for precaution, respectively. This chapter discusses a hybrid neural fuzzy network, combining unsupervised and supervised learning, for designing classifier systems. Based on systematic feature analysis, which is crucial for data mining and knowledge extraction, the proposed scheme signifies a novel algebraic system identification method, which can be used for knowledge extraction in general, and for satellite image analysis in particular. The goal of this chapter is to develop a cascaded architecture of a neural fuzzy network with feature mapping (CNFM) to help the classification of satellite images.