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.
@article{arxiv.1810.11690,
title = {3D Terrain Segmentation in the SWIR Spectrum},
author = {Dalton Rosario and Anthony Ortiz and Olac Fuentes},
journal= {arXiv preprint arXiv:1810.11690},
year = {2018}
}
Comments
Published on: IEEE Workshop on Hyperspectral Image and Signal Processing Conference (WHISPERS 2018), Amsterdam, The Netherlands, September 2018