Class-oriented spectral partitioning for hyperspectral image classification

Yi Liu; Jun Li; Antonio Plaza; Kun Tan

doi:10.1109/IGARSS.2015.7326951

Class-oriented spectral partitioning for hyperspectral image classification

Yi Liu, Jun Li, Antonio Plaza, Kun Tan

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

Abstract

This paper presents a new approach for class-oriented spectral partitioning for hyperspectral image classification. First, without empirical information, we automatically search the spectral bands that correspond to a specific class by using different band selection approaches. Then, the obtained class-oriented spectral partitions are used respectively as the input of a group of classifiers, the results of which are combined together to generate a final one by a multiple classifier system. Our experimental results, conducted with the well-known Indians Pines test site hyperspectral image collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) in NW Indiana, suggest that our presented spectral partitioning method leads to competitive results when compared with other state-of-the-art approaches.

Original language	English
Title of host publication	2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4983-4986
Number of pages	4
ISBN (Electronic)	9781479979295
DOIs	https://doi.org/10.1109/IGARSS.2015.7326951
State	Published - 10 Nov 2015
Externally published	Yes
Event	IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italy Duration: 26 Jul 2015 → 31 Jul 2015

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2015-November

Conference

Conference	IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Country/Territory	Italy
City	Milan
Period	26/07/15 → 31/07/15

Access to Document

10.1109/IGARSS.2015.7326951

Cite this

Liu, Y., Li, J., Plaza, A., & Tan, K. (2015). Class-oriented spectral partitioning for hyperspectral image classification. In 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings (pp. 4983-4986). Article 7326951 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2015-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2015.7326951

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title = "Class-oriented spectral partitioning for hyperspectral image classification",

abstract = "This paper presents a new approach for class-oriented spectral partitioning for hyperspectral image classification. First, without empirical information, we automatically search the spectral bands that correspond to a specific class by using different band selection approaches. Then, the obtained class-oriented spectral partitions are used respectively as the input of a group of classifiers, the results of which are combined together to generate a final one by a multiple classifier system. Our experimental results, conducted with the well-known Indians Pines test site hyperspectral image collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) in NW Indiana, suggest that our presented spectral partitioning method leads to competitive results when compared with other state-of-the-art approaches.",

author = "Yi Liu and Jun Li and Antonio Plaza and Kun Tan",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 ; Conference date: 26-07-2015 Through 31-07-2015",

year = "2015",

month = nov,

day = "10",

doi = "10.1109/IGARSS.2015.7326951",

language = "英语",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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Liu, Y, Li, J, Plaza, A & Tan, K 2015, Class-oriented spectral partitioning for hyperspectral image classification. in 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings., 7326951, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2015-November, Institute of Electrical and Electronics Engineers Inc., pp. 4983-4986, IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015, Milan, Italy, 26/07/15. https://doi.org/10.1109/IGARSS.2015.7326951

Class-oriented spectral partitioning for hyperspectral image classification. / Liu, Yi; Li, Jun; Plaza, Antonio et al.
2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 4983-4986 7326951 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2015-November).

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

TY - GEN

T1 - Class-oriented spectral partitioning for hyperspectral image classification

AU - Liu, Yi

AU - Li, Jun

AU - Plaza, Antonio

AU - Tan, Kun

PY - 2015/11/10

Y1 - 2015/11/10

N2 - This paper presents a new approach for class-oriented spectral partitioning for hyperspectral image classification. First, without empirical information, we automatically search the spectral bands that correspond to a specific class by using different band selection approaches. Then, the obtained class-oriented spectral partitions are used respectively as the input of a group of classifiers, the results of which are combined together to generate a final one by a multiple classifier system. Our experimental results, conducted with the well-known Indians Pines test site hyperspectral image collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) in NW Indiana, suggest that our presented spectral partitioning method leads to competitive results when compared with other state-of-the-art approaches.

AB - This paper presents a new approach for class-oriented spectral partitioning for hyperspectral image classification. First, without empirical information, we automatically search the spectral bands that correspond to a specific class by using different band selection approaches. Then, the obtained class-oriented spectral partitions are used respectively as the input of a group of classifiers, the results of which are combined together to generate a final one by a multiple classifier system. Our experimental results, conducted with the well-known Indians Pines test site hyperspectral image collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) in NW Indiana, suggest that our presented spectral partitioning method leads to competitive results when compared with other state-of-the-art approaches.

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BT - 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 26 July 2015 through 31 July 2015

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Liu Y, Li J, Plaza A, Tan K. Class-oriented spectral partitioning for hyperspectral image classification. In 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 4983-4986. 7326951. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2015.7326951

Class-oriented spectral partitioning for hyperspectral image classification

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