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An Analysis of Spatial-Spectral Dependence in Hyperspectral Autoencoders. / Laprade, William Michael; Westergaard, Jesper Cairo; Nielsen, Jon; Nielsen, Mads; Dahl, Anders Bjorholm.
Image Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings. red. / Rikke Gade; Michael Felsberg; Joni-Kristian Kämäräinen. Springer, 2023. s. 191-202 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 13886 LNCS).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Laprade, WM
, Westergaard, JC, Nielsen, J
, Nielsen, M & Dahl, AB 2023,
An Analysis of Spatial-Spectral Dependence in Hyperspectral Autoencoders. i R Gade, M Felsberg & J-K Kämäräinen (red),
Image Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 13886 LNCS, s. 191-202, 23nd Scandinavian Conference on Image Analysis, SCIA 2023, Lapland, Finland,
18/04/2023.
https://doi.org/10.1007/978-3-031-31438-4_13APA
Laprade, W. M.
, Westergaard, J. C., Nielsen, J.
, Nielsen, M., & Dahl, A. B. (2023).
An Analysis of Spatial-Spectral Dependence in Hyperspectral Autoencoders. I R. Gade, M. Felsberg, & J-K. Kämäräinen (red.),
Image Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings (s. 191-202). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 13886 LNCS
https://doi.org/10.1007/978-3-031-31438-4_13Vancouver
Laprade WM
, Westergaard JC, Nielsen J
, Nielsen M, Dahl AB.
An Analysis of Spatial-Spectral Dependence in Hyperspectral Autoencoders. I Gade R, Felsberg M, Kämäräinen J-K, red., Image Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings. Springer. 2023. s. 191-202. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 13886 LNCS).
https://doi.org/10.1007/978-3-031-31438-4_13Author
Laprade, William Michael ; Westergaard, Jesper Cairo ; Nielsen, Jon ; Nielsen, Mads ; Dahl, Anders Bjorholm. / An Analysis of Spatial-Spectral Dependence in Hyperspectral Autoencoders. Image Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings. red. / Rikke Gade ; Michael Felsberg ; Joni-Kristian Kämäräinen. Springer, 2023. s. 191-202 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 13886 LNCS).
Bibtex
@inproceedings{824a155abee74e8689ae7b90a10e5def,
title = "An Analysis of Spatial-Spectral Dependence in Hyperspectral Autoencoders",
abstract = "Hyperspectral imaging is central for remote sensing, and much research has been done on analysis methods for land surveillance using space- and air-borne imaging systems. Proximal hyperspectral imaging is also widely used in plant and agriculture science. It allows the remote capturing of leaf reflectance information in order to determine and classify plant health and disease. With the high information density in hyperspectral images, it becomes increasingly important to apply sophisticated feature extraction in order to reduce image dimensionality while retaining useful information. Autoencoders are one of the primary methods for deep learning-based feature extraction in hyperspectral images. We investigate different setups of autoencoders to encode the spatial and spectral dimensions in different orders and ways. To our surprise, the best turns out to be a 3D CNN, where the spectral dimension is treated in the same way as the spatial dimensions.",
author = "Laprade, {William Michael} and Westergaard, {Jesper Cairo} and Jon Nielsen and Mads Nielsen and Dahl, {Anders Bjorholm}",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 23nd Scandinavian Conference on Image Analysis, SCIA 2023 ; Conference date: 18-04-2023 Through 21-04-2023",
year = "2023",
doi = "10.1007/978-3-031-31438-4_13",
language = "English",
isbn = "9783031314377",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "191--202",
editor = "Rikke Gade and Michael Felsberg and Joni-Kristian K{\"a}m{\"a}r{\"a}inen",
booktitle = "Image Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings",
address = "Switzerland",
}
RIS
TY - GEN
T1 - An Analysis of Spatial-Spectral Dependence in Hyperspectral Autoencoders
AU - Laprade, William Michael
AU - Westergaard, Jesper Cairo
AU - Nielsen, Jon
AU - Nielsen, Mads
AU - Dahl, Anders Bjorholm
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Hyperspectral imaging is central for remote sensing, and much research has been done on analysis methods for land surveillance using space- and air-borne imaging systems. Proximal hyperspectral imaging is also widely used in plant and agriculture science. It allows the remote capturing of leaf reflectance information in order to determine and classify plant health and disease. With the high information density in hyperspectral images, it becomes increasingly important to apply sophisticated feature extraction in order to reduce image dimensionality while retaining useful information. Autoencoders are one of the primary methods for deep learning-based feature extraction in hyperspectral images. We investigate different setups of autoencoders to encode the spatial and spectral dimensions in different orders and ways. To our surprise, the best turns out to be a 3D CNN, where the spectral dimension is treated in the same way as the spatial dimensions.
AB - Hyperspectral imaging is central for remote sensing, and much research has been done on analysis methods for land surveillance using space- and air-borne imaging systems. Proximal hyperspectral imaging is also widely used in plant and agriculture science. It allows the remote capturing of leaf reflectance information in order to determine and classify plant health and disease. With the high information density in hyperspectral images, it becomes increasingly important to apply sophisticated feature extraction in order to reduce image dimensionality while retaining useful information. Autoencoders are one of the primary methods for deep learning-based feature extraction in hyperspectral images. We investigate different setups of autoencoders to encode the spatial and spectral dimensions in different orders and ways. To our surprise, the best turns out to be a 3D CNN, where the spectral dimension is treated in the same way as the spatial dimensions.
UR - http://www.scopus.com/inward/record.url?scp=85161414740&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-31438-4_13
DO - 10.1007/978-3-031-31438-4_13
M3 - Article in proceedings
AN - SCOPUS:85161414740
SN - 9783031314377
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 191
EP - 202
BT - Image Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings
A2 - Gade, Rikke
A2 - Felsberg, Michael
A2 - Kämäräinen, Joni-Kristian
PB - Springer
T2 - 23nd Scandinavian Conference on Image Analysis, SCIA 2023
Y2 - 18 April 2023 through 21 April 2023
ER -
