Imaging Spectroscopy for Scene Analysis Advances in Computer Vision and Pattern Recognition

the long version

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 formationExamines models of surface reflectance, the recovery of photometric invariants, and the estimation of the illuminant power spectrum from spectral imageryDescribes spectrum representations for the interpolation of reflectance and radiance values, and the classification of spectraReviews the use of imaging spectroscopy for material identificationExplores the recovery of reflection geometry from image reflectanceInvestigates spectro-polarimetric imagery, and the recovery of object shape and material properties using polarimetric images captured from a single viewAn 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.