GPU-accelerated one-class SVM for exploration of remote sensing data

We present a machine-learning based method for the exploration of remote sensing data. Our framework mixes an intuitive interface and a one-class support-vector machine to look for rare patterns in satellite images. It benefits from a fast implementation on the Graphics Process Unit that allows reasonable times for system-user interactions. We validate our approach with ground-truth experiments and demonstrate the method on real-world datasets. We achieve faster computations when compared with sequential implementations of the same methods (up to 80 times faster for feature extraction) and with other classification methods (such as local distribution comparison).