Using air-borne Ultra-WideBand Synthetic Aperture Radar (UWB SAR) to detection underground unexploded ordnance (UXO) has the advantages of safety and efficiency.UXO detection is composed of prescreening and discrimination.Prescreening is to extract several suspected targets from SAR imagery of wide areas and discrimination is to classify these suspected targets into UXO and clutter to reduce false alarms.In this paper

the Hidden Markov Model (HMM) kernel HyperSphere Support Vector Machine (HS-SVM) UXO discriminator is proposed.HMM kernel HS-SVM employs the structural risk minimization theory and uses hypersphere in kernel feature space to classify UXO and clutter

which can solve the two problems of a small training set and without typical clutter samples.In addition

the HMM

which describes the UXO multi-aspect feature

is used as the kernel function of HS-SVM can improve the UXO discrimination performance further.The field data processing discrimination results show that HMM kernel HS-SVM outperforms the HMM and the Gaussian kernel HS-SVM in UXO discrimination.