Low-frequency ultra-wideband synthetic aperture radar (UWB SAR) is a promising technology for landmine detection. According to the scattering characteristics of body-of-revolution (BOR) targets along with azimuth angles and incident angles, a Hidden Markov model (HMM) discrimination algorithm is proposed, using such sequential features as double-hump distance and notch frequency. First, the algorithm estimated the target scatterings in all azimuths based on regions of interest (ROI). Second, sequential aspect features were extracted by sparse time-frequency representation. Then the HMM parameters were trained with the labeled samples and the probability of occurrence was computed to discriminate suspicions targets. The experimental results indicate that the proposed algorithm is effective in BOR target discrimination. 