Spectral-Spatial Classification of Hyperspectral Image Using PCA and Gabor Filtering

Qingyu Yan; Junping Zhang; Jia Feng

doi:10.1109/IGARSS39084.2020.9324555

Spectral-Spatial Classification of Hyperspectral Image Using PCA and Gabor Filtering

Qingyu Yan, Junping Zhang, Jia Feng

Harbin Institute of Technology

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

5 Citations (Scopus)

Abstract

The combination of spectral information and spatial context is known to be a suitable way in improving classification accuracy for hyperspectral image. In this paper, a novel method using PCA and spatial filtering for the classification of hyperspectral image is proposed. Firstly, PCA is used to extract spectral information from the hyperspectral image. Secondly, spatial filters containing a set of 2-D Gabor filters and rolling guidance filters (RGF) are convolved with the principal components to extract the subtle spatial texture and edge features respectively. Thirdly, the obtained features are concatenated together as a feature cube to be classified by SVM. The proposed method is thus named as PCA-GR. Experimental results on two real hyperspectral image data sets demonstrate the significant advantages of the proposed method over the compared ones.

Original language	English
Title of host publication	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	513-516
Number of pages	4
ISBN (Electronic)	9781728163741
DOIs	https://doi.org/10.1109/IGARSS39084.2020.9324555
Publication status	Published - Sept 26 2020
Externally published	Yes
Event	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States Duration: Sept 26 2020 → Oct 2 2020

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/Territory	United States
City	Virtual, Waikoloa
Period	9/26/20 → 10/2/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

ASJC Scopus Subject Areas

Computer Science Applications
General Earth and Planetary Sciences

Keywords

Hyperspectral image classification
rolling guidance filter
spatial texture information

Access to Document

10.1109/IGARSS39084.2020.9324555

Cite this

Yan, Q., Zhang, J., & Feng, J. (2020). Spectral-Spatial Classification of Hyperspectral Image Using PCA and Gabor Filtering. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (pp. 513-516). Article 9324555 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS39084.2020.9324555

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Yan, Q, Zhang, J & Feng, J 2020, Spectral-Spatial Classification of Hyperspectral Image Using PCA and Gabor Filtering. in 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings., 9324555, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 513-516, 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Virtual, Waikoloa, United States, 9/26/20. https://doi.org/10.1109/IGARSS39084.2020.9324555

Spectral-Spatial Classification of Hyperspectral Image Using PCA and Gabor Filtering. / Yan, Qingyu; Zhang, Junping; Feng, Jia.
2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 513-516 9324555 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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

TY - GEN

T1 - Spectral-Spatial Classification of Hyperspectral Image Using PCA and Gabor Filtering

AU - Yan, Qingyu

AU - Zhang, Junping

AU - Feng, Jia

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N2 - The combination of spectral information and spatial context is known to be a suitable way in improving classification accuracy for hyperspectral image. In this paper, a novel method using PCA and spatial filtering for the classification of hyperspectral image is proposed. Firstly, PCA is used to extract spectral information from the hyperspectral image. Secondly, spatial filters containing a set of 2-D Gabor filters and rolling guidance filters (RGF) are convolved with the principal components to extract the subtle spatial texture and edge features respectively. Thirdly, the obtained features are concatenated together as a feature cube to be classified by SVM. The proposed method is thus named as PCA-GR. Experimental results on two real hyperspectral image data sets demonstrate the significant advantages of the proposed method over the compared ones.

AB - The combination of spectral information and spatial context is known to be a suitable way in improving classification accuracy for hyperspectral image. In this paper, a novel method using PCA and spatial filtering for the classification of hyperspectral image is proposed. Firstly, PCA is used to extract spectral information from the hyperspectral image. Secondly, spatial filters containing a set of 2-D Gabor filters and rolling guidance filters (RGF) are convolved with the principal components to extract the subtle spatial texture and edge features respectively. Thirdly, the obtained features are concatenated together as a feature cube to be classified by SVM. The proposed method is thus named as PCA-GR. Experimental results on two real hyperspectral image data sets demonstrate the significant advantages of the proposed method over the compared ones.

KW - Hyperspectral image classification

KW - rolling guidance filter

KW - spatial texture information

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Yan Q, Zhang J, Feng J. Spectral-Spatial Classification of Hyperspectral Image Using PCA and Gabor Filtering. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 513-516. 9324555. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS39084.2020.9324555

Spectral-Spatial Classification of Hyperspectral Image Using PCA and Gabor Filtering

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this