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

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

69 Citations (Scopus)

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.

Original language	English
Title of host publication	International Geoscience and Remote Sensing Symposium (IGARSS)
Publisher	IEEE
Pages	5067-5070
Volume	2016-November
ISBN (Print)	9781509033324
DOIs	https://doi.org/10.1109/IGARSS.2016.7730321
Publication status	Published - 1 Nov 2016
Externally published	Yes
Event	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China Duration: 10 Jul 2016 → 15 Jul 2016

Publication series

Name
Volume	2016-November

Conference

Conference	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Place	China
City	Beijing
Period	10/07/16 → 15/07/16

Research Keywords

Convolutional Neural Networks
deep learning
hyperspectral classification

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10.1109/IGARSS.2016.7730321

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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",

year = "2016",

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day = "1",

doi = "10.1109/IGARSS.2016.7730321",

language = "English",

isbn = "9781509033324",

volume = "2016-November",

publisher = "IEEE",

pages = "5067--5070",

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

address = "United States",

note = "36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",

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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. in International Geoscience and Remote Sensing Symposium (IGARSS). vol. 2016-November, 7730321, IEEE, pp. 5067-5070, 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, China, 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 et al.
International Geoscience and Remote Sensing Symposium (IGARSS). Vol. 2016-November IEEE, 2016. p. 5067-5070 7730321.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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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U2 - 10.1109/IGARSS.2016.7730321

DO - 10.1109/IGARSS.2016.7730321

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781509033324

VL - 2016-November

SP - 5067

EP - 5070

BT - International Geoscience and Remote Sensing Symposium (IGARSS)

PB - IEEE

T2 - 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016

Y2 - 10 July 2016 through 15 July 2016

ER -

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

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