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Imaging Spectroscopy for Scene Analysis

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

In this chapter, we treat scenes as being composed of a tapestry of materials which can then be unmixed into end members. This leads to a treatment by which materials are intrinsic to the scene. Subsequently, we elaborate on how consistency may be imposed on the scene materials and by departing from the linear unmixing model, we address the problem of unmixing with non-negative constraints. We provide a geometric interpretation of the problem, which allows us to review these methods and their relation to simplex-based approaches elsewhere in the literature. We then turn our attention to the use of absorptions for material discovery by spectral feature fitting and spectral angle mappers. We finish the chapter by tackling discriminative band selection; we do this by making use of the Rényi entropy and classifier fusion.

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Authors and Affiliations

  1. National ICT Australia, Canberra, ACT, Australia

    Antonio Robles-Kelly & Cong Phuoc Huynh

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  1. Antonio Robles-Kelly
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  2. Cong Phuoc Huynh
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Robles-Kelly, A., Huynh, C.P. (2013). Material Discovery. In: Imaging Spectroscopy for Scene Analysis. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4652-0_8

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  • DOI: https://doi.org/10.1007/978-1-4471-4652-0_8

  • Publisher Name: Springer, London

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  • Online ISBN: 978-1-4471-4652-0

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