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Finding Endmembers in Hyperspectral Imagery

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Real-Time Progressive Hyperspectral Image Processing

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Abstract

Endmembers, defined as pure signatures, can be used to specify distinct spectral classes of interest in the data, thus providing crucial information in hyperspectral data exploitation. Technically speaking, an endmember is generally considered as a calibrated spectral signature in a data base or spectral library and is not necessarily to be a real data sample vector. If an endmember occurs as a real data sample vector or a pixel vector, it is referred to as an endmember sample vector or endmember pixel vector. Unfortunately, because of inevitable physical effects during data acquisition, it is often the case that pure sample vectors are nearly impossible to be found in a real data set in which case no endmembers can be extracted from the data set. So, using endmember extraction as a general terminology in hyperspectral image analysis is misleading. To address this issue, this chapter adopts the terminology of endmember finding to reflect more accurately what an algorithm is designed to accomplish and further explores various tasks that can be performed on finding endmembers.

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Authors and Affiliations

  1. Baltimore, Maryland, USA

    Chein-I Chang

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  1. Chein-I Chang
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Correspondence to Chein-I Chang .

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Chang, CI. (2016). Finding Endmembers in Hyperspectral Imagery. In: Real-Time Progressive Hyperspectral Image Processing. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6187-7_3

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  • DOI: https://doi.org/10.1007/978-1-4419-6187-7_3

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  • Online ISBN: 978-1-4419-6187-7

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