Classification of Hyperspectral Images by Gabor Filtering Based Deep Network

In this paper, a novel spectral-spatial classification method based on Gabor filtering and deep network (GFDN) is proposed. First, Gabor features are extracted by performing Gabor filtering on the first three principal components of the hyperspectral image, which can typically characterize the low-l...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 11; H. 4; S. 1166 - 1178
Hauptverfasser:	Kang, Xudong, Li, Chengchao, Li, Shutao, Lin, Hui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.04.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Classification Deep learning Feature extraction Gabor filter hyperspectral image (HSI) classification Hyperspectral imaging Image classification Image filters Image reconstruction Machine learning Methods Spatial distribution stacked sparse autoencoders (SSAE) Training virtual samples
ISSN:	1939-1404, 2151-1535
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Abstract	In this paper, a novel spectral-spatial classification method based on Gabor filtering and deep network (GFDN) is proposed. First, Gabor features are extracted by performing Gabor filtering on the first three principal components of the hyperspectral image, which can typically characterize the low-level spatial structures of different orientations and scales. Then, the Gabor features and spectral features are simply stacked to form the fused features. Afterwards, deep features are captured by training a stacked sparse autoencoder deep network with the fused features obtained above as inputs. Since the number of training samples of hyperspectral images is often very limited, which negatively affects the classification performance in deep learning, an effective way of constructing virtual samples is designed to generate more training samples, automatically. By jointly utilizing both the real and virtual samples, the parameters of the deep network can be better trained and updated, which can result in classification results of higher accuracies. Experiments performed on four real hyperspectral datasets show that the proposed method outperforms several recently proposed classification methods in terms of classification accuracies.
AbstractList	In this paper, a novel spectral-spatial classification method based on Gabor filtering and deep network (GFDN) is proposed. First, Gabor features are extracted by performing Gabor filtering on the first three principal components of the hyperspectral image, which can typically characterize the low-level spatial structures of different orientations and scales. Then, the Gabor features and spectral features are simply stacked to form the fused features. Afterwards, deep features are captured by training a stacked sparse autoencoder deep network with the fused features obtained above as inputs. Since the number of training samples of hyperspectral images is often very limited, which negatively affects the classification performance in deep learning, an effective way of constructing virtual samples is designed to generate more training samples, automatically. By jointly utilizing both the real and virtual samples, the parameters of the deep network can be better trained and updated, which can result in classification results of higher accuracies. Experiments performed on four real hyperspectral datasets show that the proposed method outperforms several recently proposed classification methods in terms of classification accuracies.
Author	Kang, Xudong Li, Chengchao Li, Shutao Lin, Hui
Author_xml	– sequence: 1 givenname: Xudong orcidid: 0000-0002-3807-2531 surname: Kang fullname: Kang, Xudong email: xudong_kang@163.com organization: Electrical and Information Engineering, Hunan University, Changsha, China – sequence: 2 givenname: Chengchao orcidid: 0000-0002-0585-9848 surname: Li fullname: Li, Chengchao email: chengchao_li@163.com organization: Electrical and Information Engineering, Hunan University, Changsha, China – sequence: 3 givenname: Shutao surname: Li fullname: Li, Shutao email: shutao_li@hnu.edu.cn organization: Electrical and Information Engineering, Hunan University, Changsha, China – sequence: 4 givenname: Hui orcidid: 0000-0003-2351-4461 surname: Lin fullname: Lin, Hui email: linhui1965@126.com organization: Electrical and Information Engineering, Hunan University, Changsha, China
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Snippet	In this paper, a novel spectral-spatial classification method based on Gabor filtering and deep network (GFDN) is proposed. First, Gabor features are extracted...
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SubjectTerms	Classification Deep learning Feature extraction Gabor filter hyperspectral image (HSI) classification Hyperspectral imaging Image classification Image filters Image reconstruction Machine learning Methods Spatial distribution stacked sparse autoencoders (SSAE) Training virtual samples
Title	Classification of Hyperspectral Images by Gabor Filtering Based Deep Network
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