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Material classification for 3D objects in aerial hyperspectral images
By: Healey, G.; Slater, D.;
1999 / IEEE / 0-7695-0149-4
Description
This item was taken from the IEEE Conference ' Material classification for 3D objects in aerial hyperspectral images ' Automated material classification 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.
Related Topics
Image Recognition
Vegetation Mapping
Vegetation
3d Objects
Aerial Hyperspectral Images
Material Classification
Target Recognition
Geospatial Database Construction
Hydice Imagery
Roofing Materials
Roads
Hyperspectral Imaging
Building Materials
Target Recognition
Image Databases
Spatial Databases
Information Resources
Layout
Information Geometry
Roads
Vegetation Mapping
Image Classification
Remote Sensing
Engineering
Single Spectral Radiance Function