  We focus on the automatic 3D terrain segmentation problem using hyperspectral
shortwave IR (HS-SWIR) imagery and 3D Digital Elevation Models (DEM). The
datasets were independently collected, and metadata for the HS-SWIR dataset are
unavailable. We explore an overall slope of the SWIR spectrum that correlates
with the presence of moisture in soil to propose a band ratio test to be used
as a proxy for soil moisture content to distinguish two broad classes of
objects: live vegetation from impermeable manmade surface. We show that image
based localization techniques combined with the Optimal Randomized RANdom
Sample Consensus (RANSAC) algorithm achieve precise spatial matches between
HS-SWIR data of a portion of downtown Los Angeles (LA (USA)) and the Visible
image of a geo-registered 3D DEM, covering a wider-area of LA. Our
spectral-elevation rule based approach yields an overall accuracy of 97.7%,
segmenting the object classes into buildings, houses, trees, grass, and
roads/parking lots.
