In contrast with trichromatic image sensors, imaging
spectroscopy can capture the properties of the materials in a
scene. This implies that scene analysis using imaging spectroscopy
has the capacity to robustly encode material signatures, infer
object composition and recover photometric parameters.
This landmark text/reference presents a detailed analysis of
spectral imaging, describing how it can be used in elegant and
efficient ways for the purposes of material identification, object
recognition and scene understanding. The opportunities and
challenges of combining spatial and spectral information are
explored in depth, as are a wide range of applications from
surveillance and computational photography, to biosecurity and
resource exploration.
Topics and features: discusses spectral image acquisition by
hyperspectral cameras, and the process of spectral image formation;
examines models of surface reflectance, the recovery of photometric
invariants, and the estimation of the illuminant power spectrum
from spectral imagery; describes spectrum representations for the
interpolation of reflectance and radiance values, and the
classification of spectra; reviews the use of imaging spectroscopy
for material identification; explores the recovery of reflection
geometry from image reflectance; investigates spectro-polarimetric
imagery, and the recovery of object shape and material properties
using polarimetric images captured from a single view.
An essential resource for researchers and graduate students of
computer vision and pattern recognition, this comprehensive
introduction to imaging spectroscopy for scene analysis will also
be of great use to practitioners interested in shape analysis
employing polarimetric imaging, and material recognition and
classification using hyperspectral or multispectral data.
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