Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification

Shaohui Mei; Jingyu Ji; Qianqian Bi; Junhui Hou; Qian Du; Wei Li

doi:10.1109/IGARSS.2016.7730321

Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification

Shaohui Mei, Jingyu Ji, Qianqian Bi, Junhui Hou, Qian Du, Wei Li

电子信息学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

66 引用（Scopus）

摘要

Deep convolutional neural networks (CNNs) have brought in achievements in image classification and tar- get detection. In this paper, we propose a novel five-layer CNN for hyperspectral classification by encountering recent achievement in deep learning area, such as batch normaliza- tion, dropout, Parametric Rectified Linear Unit (PReLu) acti- vation function. By taking advantage of the specific charac- teristics of hyperspectral images, spatial context and spectral information are elegantly integrated into the framework. Ex- perimental results demonstrate that our proposed CNN out- performs the state-of-the-art methods.

源语言	英语
主期刊名	2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
出版商	Institute of Electrical and Electronics Engineers Inc.
页	5067-5070
页数	4
ISBN（电子版）	9781509033324
DOI	https://doi.org/10.1109/IGARSS.2016.7730321
出版状态	已出版 - 1 11月 2016
活动	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, 中国期限: 10 7月 2016 → 15 7月 2016

出版系列

姓名	International Geoscience and Remote Sensing Symposium (IGARSS)
卷	2016-November

会议

会议	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
国家/地区	中国
市	Beijing
时期	10/07/16 → 15/07/16

访问文件

10.1109/IGARSS.2016.7730321

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链接到 Scopus 的出版物

引用此

Mei, S., Ji, J., Bi, Q., Hou, J., Du, Q., & Li, W. (2016). Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification. 在 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings (页码 5067-5070). 文章 7730321 (International Geoscience and Remote Sensing Symposium (IGARSS); 卷 2016-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2016.7730321

Mei, Shaohui ; Ji, Jingyu ; Bi, Qianqian 等. / Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification. 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 页码 5067-5070 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{75d7b5681c204db6ad6d345586d00ce2,

title = "Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification",

abstract = "Deep convolutional neural networks (CNNs) have brought in achievements in image classification and tar- get detection. In this paper, we propose a novel five-layer CNN for hyperspectral classification by encountering recent achievement in deep learning area, such as batch normaliza- tion, dropout, Parametric Rectified Linear Unit (PReLu) acti- vation function. By taking advantage of the specific charac- teristics of hyperspectral images, spatial context and spectral information are elegantly integrated into the framework. Ex- perimental results demonstrate that our proposed CNN out- performs the state-of-the-art methods.",

keywords = "Convolutional Neural Networks, deep learning, hyperspectral classification",

author = "Shaohui Mei and Jingyu Ji and Qianqian Bi and Junhui Hou and Qian Du and Wei Li",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",

year = "2016",

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doi = "10.1109/IGARSS.2016.7730321",

language = "英语",

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

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booktitle = "2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings",

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Mei, S, Ji, J, Bi, Q, Hou, J, Du, Q & Li, W 2016, Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification. 在 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings., 7730321, International Geoscience and Remote Sensing Symposium (IGARSS), 卷 2016-November, Institute of Electrical and Electronics Engineers Inc., 页码 5067-5070, 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, 中国, 10/07/16. https://doi.org/10.1109/IGARSS.2016.7730321

Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification. / Mei, Shaohui; Ji, Jingyu; Bi, Qianqian 等.
2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 页码 5067-5070 7730321 (International Geoscience and Remote Sensing Symposium (IGARSS); 卷 2016-November).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification

AU - Mei, Shaohui

AU - Ji, Jingyu

AU - Bi, Qianqian

AU - Hou, Junhui

AU - Du, Qian

AU - Li, Wei

PY - 2016/11/1

Y1 - 2016/11/1

N2 - Deep convolutional neural networks (CNNs) have brought in achievements in image classification and tar- get detection. In this paper, we propose a novel five-layer CNN for hyperspectral classification by encountering recent achievement in deep learning area, such as batch normaliza- tion, dropout, Parametric Rectified Linear Unit (PReLu) acti- vation function. By taking advantage of the specific charac- teristics of hyperspectral images, spatial context and spectral information are elegantly integrated into the framework. Ex- perimental results demonstrate that our proposed CNN out- performs the state-of-the-art methods.

AB - Deep convolutional neural networks (CNNs) have brought in achievements in image classification and tar- get detection. In this paper, we propose a novel five-layer CNN for hyperspectral classification by encountering recent achievement in deep learning area, such as batch normaliza- tion, dropout, Parametric Rectified Linear Unit (PReLu) acti- vation function. By taking advantage of the specific charac- teristics of hyperspectral images, spatial context and spectral information are elegantly integrated into the framework. Ex- perimental results demonstrate that our proposed CNN out- performs the state-of-the-art methods.

KW - Convolutional Neural Networks

KW - deep learning

KW - hyperspectral classification

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BT - 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings

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Mei S, Ji J, Bi Q, Hou J, Du Q, Li W. Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification. 在 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. 页码 5067-5070. 7730321. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2016.7730321

Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification

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