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Recursive Hyperspectral Sample Processing of Maximum Likelihood Estimation

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Real-Time Recursive Hyperspectral Sample and Band Processing

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Abstract

Chapter 9 presents a theory of recursive hyperspectral sample processing of linear spectral mixture analysis (RHSP-LSMA) to form an adaptive linear mixing model (ALMM) that can adapt to the signatures, referred to as virtual signatures (VSs), generated directly from data in an unsupervised and recursive manner. This chapter considers an alternative approach to RHSP-LSMA, called recursive hyperspectral sample processing of maximal likelihood estimation (RHSP-MLE), that uses MLE error instead of orthogonal projection (OP) residual used by recursive hyperspectral sample processing of OSP (RHSP-OSP) in Chap. 8 and least-squares error (LSE) used by RHSP-LSMA as a criterion to generate VSs. Following approaches similar to those described in Chaps. 8 and 9, this chapter develops a theory of RHSP-MLE in conjunction with ALMM to generate unknown VSs recursively in an unsupervised manner, as RHSP-LSMA does, while implementing a binary composite hypothesis testing-based Neyman–Pearson detector (NPD) at the same time to automatically determine when RHSP-MLE should stop generating VSs.

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Authors and Affiliations

  1. Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information and Technology College, Dalian Maritime University, Dalian, China

    Chein-I Chang

  2. Remote Sensing Signal and Image Processing Laboratory (RSSIPL), University of Maryland, Baltimore County (UMBC), Baltimore, MD, USA

    Chein-I Chang

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  1. Chein-I Chang
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Chang, CI. (2017). Recursive Hyperspectral Sample Processing of Maximum Likelihood Estimation. In: Real-Time Recursive Hyperspectral Sample and Band Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-45171-8_10

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  • DOI: https://doi.org/10.1007/978-3-319-45171-8_10

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-45171-8

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