  Moving cameras provide multiple intensity measurements per pixel, yet often
semantic segmentation, material recognition, and object recognition do not
utilize this information. With basic alignment over several frames of a moving
camera sequence, a distribution of intensities over multiple angles is
obtained. It is well known from prior work that luminance histograms and the
statistics of natural images provide a strong material recognition cue. We
utilize per-pixel {\it angular luminance distributions} as a key feature in
discriminating the material of the surface. The angle-space sampling in a
multiview satellite image sequence is an unstructured sampling of the
underlying reflectance function of the material. For real-world materials there
is significant intra-class variation that can be managed by building a angular
luminance network (AngLNet). This network combines angular reflectance cues
from multiple images with spatial cues as input to fully convolutional networks
for material segmentation. We demonstrate the increased performance of AngLNet
over prior state-of-the-art in material segmentation from satellite imagery.
