First investigations on detection of stationary vehicles in airborne decimeter resolution SAR data by supervised learning

In this work we investigate the automatic detection of stationary vehicles in SAR images by supervised learning algorithms. This implies the description of the vehicles by a set of representative features. We combine several classes of features including subspace projection based on clustering mechanisms (NMF, PCA), statistical features (image moments), spectral features (gabor wavelets) as well as boundary (shape analysis) and region descriptors (HOG). We further use two different learning algorithms: Support Vector Machines (SVM) and Random Forests.