Professor, Dept. of Environmental Resources Engineering, SUNY ESF

Support Vector Machines in RS problems

Support Vector Machines are a promising family of classifiers due to their low requirements of training samples, fast training times and relative high accuracy. In this review we discuss benefits and limitations and provide a collective summary of applications in the remote sensing field.

Further information: G. Mountrakis, J. Im, C. Ogole (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3):247-259. [803KB pdf]