Support Vector Machine Experiments for Road Recognition in High Resolution Images

Support Vector Machines have received considerable attention from the pattern recognition community in recent years. They have been applied to various classical recognition problems achieving comparable or even superior results to classifiers such as neural networks. We investigate the application of Support Vector Machines (SVMs) to the problem of road recognition from remotely sensed images using edge-based features. We present very encouraging results from our experiments, which are comparable to decision tree and neural network classifiers.