Parameter Optimization using Genetic Algorithm for Classification of Multispectral Satellite Images
The use of multisource remote sensing data for improved land cover classification has attracted the attention of many researchers. On the other hand, such an approach increases the data volume with more exceed information and increased levels of uncertainty within datasets, which may actually reduce the classification accuracy. Support vector machine (SVM) is originally developed for linear two-class classification via constructing an optimal separating hyper plane, where the margin is maximal. Genetic Algorithm (GA) is a stochastic and heuristic searching algorithm that is inspired by natural evolution. By using GA along with SVM here we are trying to make classification of the objects such that it will be closer to the original image. Classification of multispectral remotely sensed data and investigated with a special focus on uncertainty analysis in the produced land cover maps.
Keywords - Multi-spectral satellite imagery, Support Vector Machine (SVM), Genetic Algorithm (GA)