Deep learning for remote sensing image classificationA survey

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Deep learning for remote sensing image classification : A survey. / Li, Ying; Zhang, Haokui; Xue, Xizhe; Jiang, Yenan; Shen, Qiang.

In: WIREs Data Mining and Knowledge Discovery, Vol. 8, No. 6, e1264, 01.11.2018.

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Harvard

Li, Y, Zhang, H, Xue, X, Jiang, Y & Shen, Q 2018, 'Deep learning for remote sensing image classification: A survey' WIREs Data Mining and Knowledge Discovery, vol. 8, no. 6, e1264. https://doi.org/10.1002/widm.1264

APA

Li, Y., Zhang, H., Xue, X., Jiang, Y., & Shen, Q. (2018). Deep learning for remote sensing image classification: A survey. WIREs Data Mining and Knowledge Discovery, 8(6), [e1264]. https://doi.org/10.1002/widm.1264

Vancouver

Li Y, Zhang H, Xue X, Jiang Y, Shen Q. Deep learning for remote sensing image classification: A survey. WIREs Data Mining and Knowledge Discovery. 2018 Nov 1;8(6). e1264. https://doi.org/10.1002/widm.1264

Author

Li, Ying ; Zhang, Haokui ; Xue, Xizhe ; Jiang, Yenan ; Shen, Qiang. / Deep learning for remote sensing image classification : A survey. In: WIREs Data Mining and Knowledge Discovery. 2018 ; Vol. 8, No. 6.

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@article{81f91d8881c246d4981f8942fc13c712,
title = "Deep learning for remote sensing image classification: A survey",
abstract = "Remote sensing (RS) image classification plays an important role in the earth observation technology using RS data, having been widely exploited in both military and civil fields. However, due to the characteristics of RS data such as high dimensionality and relatively small amounts of labeled samples available, performing RS image classification faces great scientific and practical challenges. In recent years, as new deep learning (DL) techniques emerge, approaches to RS image classification with DL have achieved significant breakthroughs, offering novel opportunities for the research and development of RS image classification. In this paper, a brief overview of typical DL models is presented first. This is followed by a systematic review of pixel‐wise and scene‐wise RS image classification approaches that are based on the use of DL. A comparative analysis regarding the performances of typical DL‐based RS methods is also provided. Finally, the challenges and potential directions for further research are discussed",
keywords = "convolutional neural network, deep belief network, deep learning, pixel-wise classification, remote sensing image, scene classification, stacked auto-encoder",
author = "Ying Li and Haokui Zhang and Xizhe Xue and Yenan Jiang and Qiang Shen",
year = "2018",
month = "11",
day = "1",
doi = "10.1002/widm.1264",
language = "English",
volume = "8",
journal = "WIREs Data Mining and Knowledge Discovery",
issn = "1942-4795",
publisher = "Wiley",
number = "6",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Deep learning for remote sensing image classification

T2 - A survey

AU - Li, Ying

AU - Zhang, Haokui

AU - Xue, Xizhe

AU - Jiang, Yenan

AU - Shen, Qiang

PY - 2018/11/1

Y1 - 2018/11/1

N2 - Remote sensing (RS) image classification plays an important role in the earth observation technology using RS data, having been widely exploited in both military and civil fields. However, due to the characteristics of RS data such as high dimensionality and relatively small amounts of labeled samples available, performing RS image classification faces great scientific and practical challenges. In recent years, as new deep learning (DL) techniques emerge, approaches to RS image classification with DL have achieved significant breakthroughs, offering novel opportunities for the research and development of RS image classification. In this paper, a brief overview of typical DL models is presented first. This is followed by a systematic review of pixel‐wise and scene‐wise RS image classification approaches that are based on the use of DL. A comparative analysis regarding the performances of typical DL‐based RS methods is also provided. Finally, the challenges and potential directions for further research are discussed

AB - Remote sensing (RS) image classification plays an important role in the earth observation technology using RS data, having been widely exploited in both military and civil fields. However, due to the characteristics of RS data such as high dimensionality and relatively small amounts of labeled samples available, performing RS image classification faces great scientific and practical challenges. In recent years, as new deep learning (DL) techniques emerge, approaches to RS image classification with DL have achieved significant breakthroughs, offering novel opportunities for the research and development of RS image classification. In this paper, a brief overview of typical DL models is presented first. This is followed by a systematic review of pixel‐wise and scene‐wise RS image classification approaches that are based on the use of DL. A comparative analysis regarding the performances of typical DL‐based RS methods is also provided. Finally, the challenges and potential directions for further research are discussed

KW - convolutional neural network

KW - deep belief network

KW - deep learning

KW - pixel-wise classification

KW - remote sensing image

KW - scene classification

KW - stacked auto-encoder

U2 - 10.1002/widm.1264

DO - 10.1002/widm.1264

M3 - Article

VL - 8

JO - WIREs Data Mining and Knowledge Discovery

JF - WIREs Data Mining and Knowledge Discovery

SN - 1942-4795

IS - 6

M1 - e1264

ER -

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