New feature for vehicle target discrimination in SAR imagery
一种SAR图像车辆目标鉴别特征及其提取方法

The differential box-counting algorithm is introduced to calculate a new discriminating feature named Lacunarity, which is used to distinguish vehicle targetfrom naturalclutter in high-resolution SAR imagery in thispaper. 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 realvehicle targetdata and natural terrain sdata inMSTAR database are applied to test the above algorithm. The discrimination performance using lacunarity is compared withHausdorff dimension. The result shows that lacunarity is a good discriminating feature, which can eliminatemost false alarms from natural terrains andmost interference from theman-made targetswith low false alarm probability.