Lacunarity as a texture measure for SAR imagery

Lacunarity analysis is a simple technique for characterizing texture in binary images. Lacunarity quantifies deviation from translational invariance by describing the distribution of gaps within the image at multiple scales: the more lacunar an image, the more heterogeneous the spatial arrangement of gaps. For grey-level data, a series of binary images are formed through slicing the image histogram by quantiles. Characteristic decays of lacunarity as a function of window size permit scene object texture to be distinguished from speckle. Using a series of ERS-1 SAR images of the Brazilian Pantanal, we demonstrate how lacunarity functions can link image phenomenology with scene dynamics.