Target detection in hyperspectral images using support vector neural networks algorithm

In this study, the use of Support Vector Neural Network (SVNN) algorithm is offered for target detection process in HSI. The basic principle in classification algorithms is using characteristics of the data to find classification function that separate the data from each other. Neural Networks are among the non-linear classification method that can perform with high success. The classification success depends on the training data and the training algorithm that are used. SVNN Algorithm is one of the methods used to increase the classification margin of the NNs. In this algorithm is provided a training method that used eigenvalue decay that provides a margin maximization as in SVM for NNs. In this context, a minimization problem that provide margin maximization for target detection in Hyperspectral images is defined and this problem is solved by Genetic Algorithms. In this way an algorithm that has high classification performance arises.