On Hyperspectral Image Classification and Deep Learning

K. Prema; A. V. Sriharsha

doi:10.1007/978-3-031-78940-3_22

On Hyperspectral Image Classification and Deep Learning

K. Prema
, A. V. Sriharsha^*

^*Corresponding author for this work

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Hyperspectral imaging is a technique for capturing images of objects in multiple dimensions. It combines spectroscopic and imaging technology to produce multidimensional images. Hyperspectral imaging (HSI) allows us to examine any object's internal and external characteristics. Every characteristic of an object has a distinctive spectral signature that is created as a result of variations in the reflectance or emittance of the object's material. Hyperspectral imaging (HSI), which is non-destructive, is now being used in the food production, medical diagnosis, precision agriculture, pharmaceutical, recycling, and environmental monitoring industries. The various HSI classification techniques based on conventional, deep learning, and pre-trained classifiers will be reviewed.

Original language	English
Title of host publication	Bio-Inspired Computing - Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2023
Editors	Anu Bajaj, Ajith Abraham, Ryotaro Kamimura
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	220-228
Number of pages	9
ISBN (Print)	9783031789397
DOIs	https://doi.org/10.1007/978-3-031-78940-3_22
Publication status	Published - 2025
Event	14th International Conference on Innovations in Bio-Inspired Computing and Applications and 13th World Congress on Information and Communication Technologies, IBICA-WICT 2023 - Kochi, India Duration: 14-12-2023 → 15-12-2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1229 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	14th International Conference on Innovations in Bio-Inspired Computing and Applications and 13th World Congress on Information and Communication Technologies, IBICA-WICT 2023
Country/Territory	India
City	Kochi
Period	14-12-23 → 15-12-23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-031-78940-3_22

Cite this

Prema, K., & Sriharsha, A. V. (2025). On Hyperspectral Image Classification and Deep Learning. In A. Bajaj, A. Abraham, & R. Kamimura (Eds.), Bio-Inspired Computing - Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2023 (pp. 220-228). (Lecture Notes in Networks and Systems; Vol. 1229 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-78940-3_22

Prema, K. ; Sriharsha, A. V. / On Hyperspectral Image Classification and Deep Learning. Bio-Inspired Computing - Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2023. editor / Anu Bajaj ; Ajith Abraham ; Ryotaro Kamimura. Springer Science and Business Media Deutschland GmbH, 2025. pp. 220-228 (Lecture Notes in Networks and Systems).

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Prema, K & Sriharsha, AV 2025, On Hyperspectral Image Classification and Deep Learning. in A Bajaj, A Abraham & R Kamimura (eds), Bio-Inspired Computing - Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2023. Lecture Notes in Networks and Systems, vol. 1229 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 220-228, 14th International Conference on Innovations in Bio-Inspired Computing and Applications and 13th World Congress on Information and Communication Technologies, IBICA-WICT 2023, Kochi, India, 14-12-23. https://doi.org/10.1007/978-3-031-78940-3_22

On Hyperspectral Image Classification and Deep Learning. / Prema, K.; Sriharsha, A. V.
Bio-Inspired Computing - Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2023. ed. / Anu Bajaj; Ajith Abraham; Ryotaro Kamimura. Springer Science and Business Media Deutschland GmbH, 2025. p. 220-228 (Lecture Notes in Networks and Systems; Vol. 1229 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - Hyperspectral imaging is a technique for capturing images of objects in multiple dimensions. It combines spectroscopic and imaging technology to produce multidimensional images. Hyperspectral imaging (HSI) allows us to examine any object's internal and external characteristics. Every characteristic of an object has a distinctive spectral signature that is created as a result of variations in the reflectance or emittance of the object's material. Hyperspectral imaging (HSI), which is non-destructive, is now being used in the food production, medical diagnosis, precision agriculture, pharmaceutical, recycling, and environmental monitoring industries. The various HSI classification techniques based on conventional, deep learning, and pre-trained classifiers will be reviewed.

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Prema K, Sriharsha AV. On Hyperspectral Image Classification and Deep Learning. In Bajaj A, Abraham A, Kamimura R, editors, Bio-Inspired Computing - Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2023. Springer Science and Business Media Deutschland GmbH. 2025. p. 220-228. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-78940-3_22

On Hyperspectral Image Classification and Deep Learning

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

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