Unsupervised exploration of hyperspectral and multispectral images

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Standard

Unsupervised exploration of hyperspectral and multispectral images. / Marini, Federico; Amigo, José Manuel.

Hyperspectral Imaging. ed. / José Manuel Amigo. Elsevier, 2020. p. 93-114 (Data Handling in Science and Technology, Vol. 32).

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Harvard

Marini, F & Amigo, JM 2020, Unsupervised exploration of hyperspectral and multispectral images. in JM Amigo (ed.), Hyperspectral Imaging. Elsevier, Data Handling in Science and Technology, vol. 32, pp. 93-114. https://doi.org/10.1016/B978-0-444-63977-6.00006-7

APA

Marini, F., & Amigo, J. M. (2020). Unsupervised exploration of hyperspectral and multispectral images. In J. M. Amigo (Ed.), Hyperspectral Imaging (pp. 93-114). Elsevier. Data Handling in Science and Technology Vol. 32 https://doi.org/10.1016/B978-0-444-63977-6.00006-7

Vancouver

Marini F, Amigo JM. Unsupervised exploration of hyperspectral and multispectral images. In Amigo JM, editor, Hyperspectral Imaging. Elsevier. 2020. p. 93-114. (Data Handling in Science and Technology, Vol. 32). https://doi.org/10.1016/B978-0-444-63977-6.00006-7

Author

Marini, Federico ; Amigo, José Manuel. / Unsupervised exploration of hyperspectral and multispectral images. Hyperspectral Imaging. editor / José Manuel Amigo. Elsevier, 2020. pp. 93-114 (Data Handling in Science and Technology, Vol. 32).

Bibtex

@inbook{af0653407dd243f1bac09c3f1d9b6b82,
title = "Unsupervised exploration of hyperspectral and multispectral images",
abstract = "One of the first actions to make in the analysis of hyperspectral and multispectral images is the unsupervised exploration of the spatio-spectral domains. Unsupervised exploration techniques are methods that obtain information about the spatial distribution of compounds on the images, some of their spectral signatures, their main sources of variation, and also help to detect defectuous pixels or spectra, by only using the spatial and spectral information of the images acquired in an unsupervised manner. In this chapter, we present the most popular methods for unsupervised modeling together with examples to understand their major benefits and drawbacks.",
keywords = "Clusters, Dendrograms, Fuzzy clustering, K-means, Multivariate data analysis, PCA, Unsupervised",
author = "Federico Marini and Amigo, {Jos{\'e} Manuel}",
year = "2020",
doi = "10.1016/B978-0-444-63977-6.00006-7",
language = "English",
isbn = "978-0-444-63977-6",
series = "Data Handling in Science and Technology",
publisher = "Elsevier",
pages = "93--114",
editor = "Amigo, {Jos{\'e} Manuel}",
booktitle = "Hyperspectral Imaging",
address = "Netherlands",

}

RIS

TY - CHAP

T1 - Unsupervised exploration of hyperspectral and multispectral images

AU - Marini, Federico

AU - Amigo, José Manuel

PY - 2020

Y1 - 2020

N2 - One of the first actions to make in the analysis of hyperspectral and multispectral images is the unsupervised exploration of the spatio-spectral domains. Unsupervised exploration techniques are methods that obtain information about the spatial distribution of compounds on the images, some of their spectral signatures, their main sources of variation, and also help to detect defectuous pixels or spectra, by only using the spatial and spectral information of the images acquired in an unsupervised manner. In this chapter, we present the most popular methods for unsupervised modeling together with examples to understand their major benefits and drawbacks.

AB - One of the first actions to make in the analysis of hyperspectral and multispectral images is the unsupervised exploration of the spatio-spectral domains. Unsupervised exploration techniques are methods that obtain information about the spatial distribution of compounds on the images, some of their spectral signatures, their main sources of variation, and also help to detect defectuous pixels or spectra, by only using the spatial and spectral information of the images acquired in an unsupervised manner. In this chapter, we present the most popular methods for unsupervised modeling together with examples to understand their major benefits and drawbacks.

KW - Clusters

KW - Dendrograms

KW - Fuzzy clustering

KW - K-means

KW - Multivariate data analysis

KW - PCA

KW - Unsupervised

U2 - 10.1016/B978-0-444-63977-6.00006-7

DO - 10.1016/B978-0-444-63977-6.00006-7

M3 - Book chapter

AN - SCOPUS:85072712384

SN - 978-0-444-63977-6

T3 - Data Handling in Science and Technology

SP - 93

EP - 114

BT - Hyperspectral Imaging

A2 - Amigo, José Manuel

PB - Elsevier

ER -

ID: 231241111