Hyperspectral image classification using Gradient Local Auto-Correlations

Chen Chen; Junjun Jiang; Baochang Zhang; Wankou Yang; Jianzhong Guo

doi:10.1109/ACPR.2015.7486544

Hyperspectral image classification using Gradient Local Auto-Correlations

Chen Chen
, Junjun Jiang
, Baochang Zhang
, Wankou Yang
, Jianzhong Guo

University of Texas at Dallas
China University of Geosciences, Wuhan
Beihang University
Southeast University, Nanjing
Wuhan Textile University

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

12 Scopus citations

Abstract

Spatial information has been verified to be helpful in hyperspectral image classification. In this paper, a spatial feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is presented for hyperspectral imagery (HSI) classification. The Gradient Local Auto-Correlations (GLAC) method employs second order statistics (i.e., auto-correlations) to capture richer information from images than the histogram-based methods (e.g., Histogram of Oriented Gradients) which use first order statistics (i.e., histograms). The experiments carried out on two hyperspectral images proved the effectiveness of the proposed method compared to the state-of-the-art spatial feature extraction methods for HSI classification.

Original language	English
Title of host publication	Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	454-458
Number of pages	5
ISBN (Electronic)	9781479961009
DOIs	https://doi.org/10.1109/ACPR.2015.7486544
State	Published - 7 Jun 2016
Externally published	Yes
Event	3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015 - Kuala Lumpur, Malaysia Duration: 3 Nov 2016 → 6 Nov 2016

Publication series

Name	Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015

Conference

Conference	3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015
Country/Territory	Malaysia
City	Kuala Lumpur
Period	3/11/16 → 6/11/16

Access to Document

10.1109/ACPR.2015.7486544

Cite this

Chen, C., Jiang, J., Zhang, B., Yang, W., & Guo, J. (2016). Hyperspectral image classification using Gradient Local Auto-Correlations. In Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015 (pp. 454-458). Article 7486544 (Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACPR.2015.7486544

@inproceedings{34ec6a6e88b84baebb573fa68c74b658,

title = "Hyperspectral image classification using Gradient Local Auto-Correlations",

abstract = "Spatial information has been verified to be helpful in hyperspectral image classification. In this paper, a spatial feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is presented for hyperspectral imagery (HSI) classification. The Gradient Local Auto-Correlations (GLAC) method employs second order statistics (i.e., auto-correlations) to capture richer information from images than the histogram-based methods (e.g., Histogram of Oriented Gradients) which use first order statistics (i.e., histograms). The experiments carried out on two hyperspectral images proved the effectiveness of the proposed method compared to the state-of-the-art spatial feature extraction methods for HSI classification.",

author = "Chen Chen and Junjun Jiang and Baochang Zhang and Wankou Yang and Jianzhong Guo",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015 ; Conference date: 03-11-2016 Through 06-11-2016",

year = "2016",

month = jun,

day = "7",

doi = "10.1109/ACPR.2015.7486544",

language = "英语",

series = "Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "454--458",

booktitle = "Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015",

address = "美国",

}

Chen, C, Jiang, J, Zhang, B, Yang, W & Guo, J 2016, Hyperspectral image classification using Gradient Local Auto-Correlations. in Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015., 7486544, Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015, Institute of Electrical and Electronics Engineers Inc., pp. 454-458, 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015, Kuala Lumpur, Malaysia, 3/11/16. https://doi.org/10.1109/ACPR.2015.7486544

Hyperspectral image classification using Gradient Local Auto-Correlations. / Chen, Chen; Jiang, Junjun; Zhang, Baochang et al.
Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 454-458 7486544 (Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015).

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

TY - GEN

T1 - Hyperspectral image classification using Gradient Local Auto-Correlations

AU - Chen, Chen

AU - Jiang, Junjun

AU - Zhang, Baochang

AU - Yang, Wankou

AU - Guo, Jianzhong

PY - 2016/6/7

Y1 - 2016/6/7

N2 - Spatial information has been verified to be helpful in hyperspectral image classification. In this paper, a spatial feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is presented for hyperspectral imagery (HSI) classification. The Gradient Local Auto-Correlations (GLAC) method employs second order statistics (i.e., auto-correlations) to capture richer information from images than the histogram-based methods (e.g., Histogram of Oriented Gradients) which use first order statistics (i.e., histograms). The experiments carried out on two hyperspectral images proved the effectiveness of the proposed method compared to the state-of-the-art spatial feature extraction methods for HSI classification.

AB - Spatial information has been verified to be helpful in hyperspectral image classification. In this paper, a spatial feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is presented for hyperspectral imagery (HSI) classification. The Gradient Local Auto-Correlations (GLAC) method employs second order statistics (i.e., auto-correlations) to capture richer information from images than the histogram-based methods (e.g., Histogram of Oriented Gradients) which use first order statistics (i.e., histograms). The experiments carried out on two hyperspectral images proved the effectiveness of the proposed method compared to the state-of-the-art spatial feature extraction methods for HSI classification.

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DO - 10.1109/ACPR.2015.7486544

M3 - 会议稿件

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T3 - Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015

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BT - Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Chen C, Jiang J, Zhang B, Yang W, Guo J. Hyperspectral image classification using Gradient Local Auto-Correlations. In Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 454-458. 7486544. (Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015). doi: 10.1109/ACPR.2015.7486544

Hyperspectral image classification using Gradient Local Auto-Correlations

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