A Local Information-based Kernelized OSP Method for Target Detection

In order to optimize the background subspace and suppress the false alarm better,this paper proposes a local information-based kernelized OSP method(KLOSP) for target detection.Neighbor spatial information is brought in to construct variable optimum background projective subspace.KLOSP has acquired the best Receiver Operating Characteristics(ROC) curve in simulated data experiment and obtained the biggest target-background difference as well as the highest detection rate.It is proved that this algorithm can detect targets in hyperspectral imageries accurately and effectively in real image experiment.