3D Terrain Segmentation in the SWIR Spectrum

TL;DR

This paper presents a method for 3D terrain segmentation using hyperspectral SWIR imagery and DEMs, achieving high accuracy in classifying various land cover types in urban LA.

Contribution

It introduces a spectral-elevation rule based approach leveraging a band ratio test for soil moisture, combined with image localization and RANSAC for precise segmentation.

Findings

Achieved 97.7% overall accuracy in terrain segmentation.

Effectively distinguished vegetation from manmade surfaces.

Demonstrated precise spatial matching between hyperspectral data and 3D DEMs.

Abstract

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.