Classification with controlled robustness in high-resolution SAR data

Abstract

Ground target classification in high-resolution SAR data has become increasingly important over the years. Kernel machines like the Support Vector Machine (SVM) and the Relevance Vector Machine (RVM) afford a great chance to solve this problem. But it is not possible to customize these kernel machines. Therefore the main objective of this work has been the development of a mechanism that controls the classification quality versus the computational effort. The investigations have been carried out with usage of the MSTAR public target dataset. The result of this work is an extended RVM, the RVMG. A single parameter is controlling the robustness of the system. The spectrum varies from a machine 15 times faster and of 10% lower quality than the SVM, goes to a 5 times faster and equal quality machine, and ends with a machine a little bit faster than the SVM and of better quality than the Lagrangian Support Vector Machine (LSVM). © Springer-Verlag Berlin Heidelberg 2003.