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Supervised band selection for optimal use of data from airborne hyperspectral sensors

Supervised band selection for optimal use of data from airborne hyperspectral sensors
Supervised band selection for optimal use of data from airborne hyperspectral sensors
This paper presents a practical supervised band selection procedure for airborne imaging spectrometers and Maximum Likelihood classification (MLC) as data application. The output band set is optimal in band location, width and number regarding the MLC accuracy of the classification task. The supervised algorithm is based on feature selection and requires a user-defined class set. For two given semi-natural vegetation data and class sets, the selected band sets performed superior to established vegetation band sets used in current satellite and airborne sensors, most noticeably for the first few bands. The algorithm was implemented in IDLTM/ENVITM. It may also be used for feature selection, the generation of classdiscriminate colour composites, the prioritization of already existing band sets, and the determination of the intrinsic discriminant dimensionality of the data set.
1770-1772
IEEE
Riedmann, M.
af15d27d-d67c-4143-9952-60bc7b1898a1
Milton, E.J.
c447d4a7-b6ee-4782-a205-f240e3f5488b
Riedmann, M.
af15d27d-d67c-4143-9952-60bc7b1898a1
Milton, E.J.
c447d4a7-b6ee-4782-a205-f240e3f5488b

Riedmann, M. and Milton, E.J. (2003) Supervised band selection for optimal use of data from airborne hyperspectral sensors. In IGARSS '03 Proceedings of the International Geoscience and Remote Sensing Symposium, 21-25 July 2003, Toulouse, France. IEEE. pp. 1770-1772 .

Record type: Conference or Workshop Item (Paper)

Abstract

This paper presents a practical supervised band selection procedure for airborne imaging spectrometers and Maximum Likelihood classification (MLC) as data application. The output band set is optimal in band location, width and number regarding the MLC accuracy of the classification task. The supervised algorithm is based on feature selection and requires a user-defined class set. For two given semi-natural vegetation data and class sets, the selected band sets performed superior to established vegetation band sets used in current satellite and airborne sensors, most noticeably for the first few bands. The algorithm was implemented in IDLTM/ENVITM. It may also be used for feature selection, the generation of classdiscriminate colour composites, the prioritization of already existing band sets, and the determination of the intrinsic discriminant dimensionality of the data set.

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Published date: 2003
Additional Information: CD-ROM
Venue - Dates: Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, 2003-07-21 - 2003-07-25

Identifiers

Local EPrints ID: 6175
URI: http://eprints.soton.ac.uk/id/eprint/6175
PURE UUID: 191274d1-cf93-4d2a-9696-63c731703a11

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Date deposited: 21 May 2004
Last modified: 15 Mar 2024 04:47

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Contributors

Author: M. Riedmann
Author: E.J. Milton

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