Feature-based classification fusion of vehicles in high-resolution SAR and optical imagery

Abstract

An effective feature fusion strategy applied in vehicles classification is devised, which takes advantage of the complementary vehicle features in Synthetic aperture radar (SAR) and Optical images. With high spatial resolution SAR images, it is easy to detect vehicles fast and accurately because of the strong radar cross sections (RCS) of them compared to background. However, the classification of vehicles in SAR images can carry a significant amount of error (misclassification) since the radar scattering from a vehicle is often highly dependent on the target-sensor orientation. In contrast, optical images usually provide good classification performance exploiting the aspect dependent information. Therefore, the proposed method maps the detection results of SAR image into the co-registered optical image, and then combines the target's features from SAR and Optical images together to feature-fusion classification using the Fuzzy C-means (FCM) algorithm.