Publications

Robust terrain classification of high spatial resolution remote sensing data employing probabilistic feature fusion and pixelwise voting

West, Roger D.; Redman, Brian J.; Yocky, David A.; Vander Laan, John D.; Anderson, Dylan Z.

There are several factors that should be considered for robust terrain classification. We address the issue of high pixel-wise variability within terrain classes from remote sensing modalities, when the spatial resolution is less than one meter. Our proposed method segments an image into superpixels, makes terrain classification decisions on the pixels within each superpixel using the probabilistic feature fusion (PFF) classifier, then makes a superpixel-level terrain classification decision by the majority vote of the pixels within the superpixel. We show that this method leads to improved terrain classification decisions. We demonstrate our method on optical, hyperspectral, and polarimetric synthetic aperture radar data.