Stochastic image spectroscopy: a discriminative generative approach to hyperspectral image modelling and classification

Autor:	Alvaro F. Egaña, Alejandro Ehrenfeld, Franco Curotto, Juan F. Sánchez-Pérez, Jorge F. Silva
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Hyperspectral image analysis Hyperspectral image classification Generative modelling and image formation Directed graphical models Latent variable modelling Approximated inference Medicine Science
Zdroj:	Scientific Reports, Vol 14, Iss 1, Pp 1-29 (2024)
Druh dokumentu:	article
ISSN:	2045-2322
DOI:	10.1038/s41598-024-69732-6
Popis:	Abstract This paper introduces a new latent variable probabilistic framework for representing spectral data of high spatial and spectral dimensionality, such as hyperspectral images. We use a generative Bayesian model to represent the image formation process and provide interpretable and efficient inference and learning methods. Surprisingly, our approach can be implemented with simple tools and does not require extensive training data, detailed pixel-by-pixel labeling, or significant computational resources. Numerous experiments with simulated data and real benchmark scenarios show encouraging image classification performance. These results validate the unique ability of our framework to discriminate complex hyperspectral images, irrespective of the presence of highly discriminative spectral signatures.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/1b5285902e744f3bab103cc8f3a6311a Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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