Preprocessing of hyperspectral and multispectral images

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Standard

Preprocessing of hyperspectral and multispectral images. / Amigo, José Manuel; Santos, Carolina.

Hyperspectral Imaging. ed. / José Manuel Amigo. Elsevier, 2020. p. 37-53 (Data Handling in Science and Technology, Vol. 32).

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Harvard

Amigo, JM & Santos, C 2020, Preprocessing of hyperspectral and multispectral images. in JM Amigo (ed.), Hyperspectral Imaging. Elsevier, Data Handling in Science and Technology, vol. 32, pp. 37-53. https://doi.org/10.1016/B978-0-444-63977-6.00003-1

APA

Amigo, J. M., & Santos, C. (2020). Preprocessing of hyperspectral and multispectral images. In J. M. Amigo (Ed.), Hyperspectral Imaging (pp. 37-53). Elsevier. Data Handling in Science and Technology, Vol.. 32 https://doi.org/10.1016/B978-0-444-63977-6.00003-1

Vancouver

Amigo JM, Santos C. Preprocessing of hyperspectral and multispectral images. In Amigo JM, editor, Hyperspectral Imaging. Elsevier. 2020. p. 37-53. (Data Handling in Science and Technology, Vol. 32). https://doi.org/10.1016/B978-0-444-63977-6.00003-1

Author

Amigo, José Manuel ; Santos, Carolina. / Preprocessing of hyperspectral and multispectral images. Hyperspectral Imaging. editor / José Manuel Amigo. Elsevier, 2020. pp. 37-53 (Data Handling in Science and Technology, Vol. 32).

Bibtex

@inbook{a0b279d95e5541128ca7018dd4440031,
title = "Preprocessing of hyperspectral and multispectral images",
abstract = "Hyperspectral and multispectral images do not consist exclusively on relevant information about presence and absence of chemical compounds. Depending on the technique chosen for image acquisition, equipment, experimental/environmental conditions, among others, a variety of unwanted information such as physical phenomena or random/systematic noise will be also present in the data. These variations can be of chemical or physical nature and will generate basically two types of distortion, geometric and spectral distortions. This chapter shows the main distortions related to hyperspectral and multispectral images along with their sources of variation and how to handle them.",
keywords = "HSI, MSI, NIR, Preprocessing, Raman, Spatial aberrations, Spectral noise, Visible",
author = "Amigo, {Jos{\'e} Manuel} and Carolina Santos",
year = "2020",
doi = "10.1016/B978-0-444-63977-6.00003-1",
language = "English",
isbn = "978-0-444-63977-6",
series = "Data Handling in Science and Technology",
publisher = "Elsevier",
pages = "37--53",
editor = "Amigo, {Jos{\'e} Manuel}",
booktitle = "Hyperspectral Imaging",
address = "Netherlands",

}

RIS

TY - CHAP

T1 - Preprocessing of hyperspectral and multispectral images

AU - Amigo, José Manuel

AU - Santos, Carolina

PY - 2020

Y1 - 2020

N2 - Hyperspectral and multispectral images do not consist exclusively on relevant information about presence and absence of chemical compounds. Depending on the technique chosen for image acquisition, equipment, experimental/environmental conditions, among others, a variety of unwanted information such as physical phenomena or random/systematic noise will be also present in the data. These variations can be of chemical or physical nature and will generate basically two types of distortion, geometric and spectral distortions. This chapter shows the main distortions related to hyperspectral and multispectral images along with their sources of variation and how to handle them.

AB - Hyperspectral and multispectral images do not consist exclusively on relevant information about presence and absence of chemical compounds. Depending on the technique chosen for image acquisition, equipment, experimental/environmental conditions, among others, a variety of unwanted information such as physical phenomena or random/systematic noise will be also present in the data. These variations can be of chemical or physical nature and will generate basically two types of distortion, geometric and spectral distortions. This chapter shows the main distortions related to hyperspectral and multispectral images along with their sources of variation and how to handle them.

KW - HSI

KW - MSI

KW - NIR

KW - Preprocessing

KW - Raman

KW - Spatial aberrations

KW - Spectral noise

KW - Visible

U2 - 10.1016/B978-0-444-63977-6.00003-1

DO - 10.1016/B978-0-444-63977-6.00003-1

M3 - Book chapter

AN - SCOPUS:85072695596

SN - 978-0-444-63977-6

T3 - Data Handling in Science and Technology

SP - 37

EP - 53

BT - Hyperspectral Imaging

A2 - Amigo, José Manuel

PB - Elsevier

ER -

ID: 230896842