Inferring traffic activity from optical satellite images

In this paper we describe an approach to automatically estimate movementsof vehicles in optical satellite imagery. The approach takes advantage of the fact that the optical axes of the panchromaticand multispectral channels of current spaceborne systems like IKONOS or Quickbird are not coinciding. The time gap that appearsbetween the acquisition of the panchromatic and multispectraldata can be used to derive velocity information. We employ a sub-pixelmatching approach relying on gradient directions followed by least-squares fitting of Gaussian kernels to estimate the movement.The incorporation of the least-squares framework provides the basis to conclude about the accuracy of the movement estimates andto apply a statistical test deciding whether an object moves at all. We illustrate the matching and estimation scheme by various examplesof real data.     «

In this paper we describe an approach to automatically estimate movementsof vehicles in optical satellite imagery. The approach takes advantage of the fact that the optical axes of the panchromaticand multispectral channels of current spaceborne systems like IKONOS or Quickbird are not coinciding. The time gap that appearsbetween the acquisition of the panchromatic and multispectraldata can be used to derive velocity information. We employ a sub-pixelmatching approach relying on gradient directi...     »