Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 1717-1720 |
| Seitenumfang | 4 |
| ISBN (elektronisch) | 9781479979295 |
| Publikationsstatus | Veröffentlicht - 10 Nov. 2015 |
| Veranstaltung | IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italien Dauer: 26 Juli 2015 → 31 Juli 2015 |
Publikationsreihe
| Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
|---|---|
| Band | 2015-November |
Abstract
Hyperspectral image processing has been a very dynamic area in remote sensing and other applications since last decades. Hyperspectral images provide abundant spectral information to identify and distinguish spectrally similar materials. Recent advances in kernel machines promote the novel use of Gaussian processes (GP) for classifying hyper-spectral images. Many sophisticated kernel functions have been provided for kernel-based methods. However, different kernel functions has different performance in different applications. This paper introduces GP models with different kernel functions for classifying hyperspectral images. We first provided the mathematical formulation of GP models for classification. Then, several popular kernel functions and their hyperparaeters selection for GP models are introduced. The experiment are performed on three benchmark datasets to evaluate the performances of different kernel functions in terms of classification accuracy. Their performances are compared with each other and discussed in detailed.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Erdkunde und Planetologie (insg.)
- Allgemeine Erdkunde und Planetologie
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2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. S. 1717-1720 7326119 (International Geoscience and Remote Sensing Symposium (IGARSS); Band 2015-November).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Hyperspectral image classification using Gaussian process models
AU - Yang, Michael Ying
AU - Liao, Wentong
AU - Rosenhahn, Bodo
AU - Zhang, Zheng
PY - 2015/11/10
Y1 - 2015/11/10
N2 - Hyperspectral image processing has been a very dynamic area in remote sensing and other applications since last decades. Hyperspectral images provide abundant spectral information to identify and distinguish spectrally similar materials. Recent advances in kernel machines promote the novel use of Gaussian processes (GP) for classifying hyper-spectral images. Many sophisticated kernel functions have been provided for kernel-based methods. However, different kernel functions has different performance in different applications. This paper introduces GP models with different kernel functions for classifying hyperspectral images. We first provided the mathematical formulation of GP models for classification. Then, several popular kernel functions and their hyperparaeters selection for GP models are introduced. The experiment are performed on three benchmark datasets to evaluate the performances of different kernel functions in terms of classification accuracy. Their performances are compared with each other and discussed in detailed.
AB - Hyperspectral image processing has been a very dynamic area in remote sensing and other applications since last decades. Hyperspectral images provide abundant spectral information to identify and distinguish spectrally similar materials. Recent advances in kernel machines promote the novel use of Gaussian processes (GP) for classifying hyper-spectral images. Many sophisticated kernel functions have been provided for kernel-based methods. However, different kernel functions has different performance in different applications. This paper introduces GP models with different kernel functions for classifying hyperspectral images. We first provided the mathematical formulation of GP models for classification. Then, several popular kernel functions and their hyperparaeters selection for GP models are introduced. The experiment are performed on three benchmark datasets to evaluate the performances of different kernel functions in terms of classification accuracy. Their performances are compared with each other and discussed in detailed.
KW - Gaussian processes
KW - Hyperspectral image classification
KW - kernel function
UR - http://www.scopus.com/inward/record.url?scp=84962514599&partnerID=8YFLogxK
U2 - 10.1109/igarss.2015.7326119
DO - 10.1109/igarss.2015.7326119
M3 - Conference contribution
AN - SCOPUS:84962514599
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1717
EP - 1720
BT - 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Y2 - 26 July 2015 through 31 July 2015
ER -