16 July 1999 Material mapping for 3D objects in hyperspectral images
Author Affiliations +
Proceedings Volume 3717, Algorithms for Multispectral and Hyperspectral Imagery V; (1999); doi: 10.1117/12.353030
Event: AeroSense '99, 1999, Orlando, FL, United States
Abstract
Automated material characterization and identification from airborne imagery is an important capability for many applications including target recognition and geospatial database construction. Hyperspectral imagery provides a rich source of information for this purpose but utilization is complicated by the variability in a material's observed spectral signature due to the ambient conditions and the scene geometry. In this paper, we present a method that uses a single spectral radiance function measured from a material under unknown conditions to synthesize a comprehensive set of radiance spectra that corresponds to that material over a wide range of conditions. This set of radiance spectra can be used to build a hyperspectral subspace representation that can be used for material identification over a wide range of circumstances. We demonstrate the use of these algorithms for model synthesis and material mapping using HYDICE imagery acquired at Fort Hood, Texas. The method correctly maps several classes of roofing materials, roads, and vegetation over significant spectral changes due to variation in surface orientation. We show that the approach outperforms methods based on direct spectral comparison.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David Slater, Glenn Healey, "Material mapping for 3D objects in hyperspectral images", Proc. SPIE 3717, Algorithms for Multispectral and Hyperspectral Imagery V, (16 July 1999); doi: 10.1117/12.353030; https://doi.org/10.1117/12.353030
PROCEEDINGS
11 PAGES


SHARE
KEYWORDS
Sensors

Hyperspectral imaging

Reflectivity

Vegetation

3D image processing

Image classification

Roads

Back to Top