The differential box-counting algorithm is introduced to calculate a new discriminating feature named Lacunarity

which is used to distinguish vehicle target from natural clutter in high-resolution SAR imagery in this paper. Lacunarity feature can be used to estimate quantitatively the variation

irregularity and gap size of pixel’s intensity of candidate targets. Based on the theory of scattering center

it can be shown that the vehicle image presents more irregularity and larger gaps than natural terrain’s image. Moreover

lacunarity is robust to speckle noise and is stable under changes in intensity. Finally

the real vehicle target data and natural terrain’s data in MSTAR database are applied to test the above algorithm. The discrimination performance using lacunarity is compared with Hausdorff dimension. The result shows that lacunarity is a good discriminating feature

which can eliminate most false alarms from natural terrains and most interference from the man-made targets with low false alarm probability.