Hybrid Classification Models Using ANN and Fuzzy Support Vector Machines on Spatial Databases

Image classification is one of classical problem for many aspects of remote sensing in which to extract some of the important spatially variable parameters, global change studies and environmental applications. In the literature, various classification methods have been developed for classification of images such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Fuzzy Support Vector Machines (FSVM), Genetic algorithms (GA) and Decision Trees (DT). In this paper, we propose a combined scheme for spatial image classification, which is composed of FSVM and ANN techniques. The proposed classification scheme consists of two main steps: Firstly, we separate the each image into class by an ANN classifier based on the features of images and in the second step, the FSVM classifier has been applied on the output of ANN.  This can be denoted as ANN_FSVM classifier. A comparison of techniques for spatial data has been given in this paper.