An improved back propagation neural network approach to the remote sensing land use and land cover classification

Qiongshan Cao; Zhongyang Guo; Yong Yang

doi:10.1201/b18508-64

An improved back propagation neural network approach to the remote sensing land use and land cover classification

Qiongshan Cao
, Zhongyang Guo
, Yong Yang

地理科学学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

Land Use and Land Cover (LULC) is always an essential research focus as it is of great use in many fields. Though more and more data with high resolution can be used in LULC classification as time goes by, the remote sensing data is always useful because it contains a lot of information and can offer an overview of the study area. Thus, it is still important to find good methods to use the remote sensing data in the LULC classification. In this paper, the Back Propagation (BP) neural network improved by the Simulated Annealing (SA) algorithm is used as an approach and the result turns out to be good. This method can effectively classify the surface features into different categories.

源语言	英语
主期刊名	Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014
编辑	Ally Hu
出版商	CRC Press/Balkema
页	369-373
页数	5
ISBN（印刷版）	9781138028111
DOI	https://doi.org/10.1201/b18508-64
出版状态	已出版 - 2015
活动	Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014 - Shanghai, 中国期限: 27 12月 2014 → 28 12月 2014

出版系列

姓名	Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014

会议

会议	Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014
国家/地区	中国
市	Shanghai
时期	27/12/14 → 28/12/14

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 15 陆地生物

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10.1201/b18508-64

其它文件与链接

链接到 Scopus 的出版物

引用此

Cao, Q., Guo, Z., & Yang, Y. (2015). An improved back propagation neural network approach to the remote sensing land use and land cover classification. 在 A. Hu (编辑), Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014 (页码 369-373). (Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014). CRC Press/Balkema. https://doi.org/10.1201/b18508-64

Cao, Qiongshan ; Guo, Zhongyang ; Yang, Yong. / An improved back propagation neural network approach to the remote sensing land use and land cover classification. Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014. 编辑 / Ally Hu. CRC Press/Balkema, 2015. 页码 369-373 (Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014).

@inproceedings{65123619f42b49cb8ad9e822ad0f17e6,

title = "An improved back propagation neural network approach to the remote sensing land use and land cover classification",

abstract = "Land Use and Land Cover (LULC) is always an essential research focus as it is of great use in many fields. Though more and more data with high resolution can be used in LULC classification as time goes by, the remote sensing data is always useful because it contains a lot of information and can offer an overview of the study area. Thus, it is still important to find good methods to use the remote sensing data in the LULC classification. In this paper, the Back Propagation (BP) neural network improved by the Simulated Annealing (SA) algorithm is used as an approach and the result turns out to be good. This method can effectively classify the surface features into different categories.",

author = "Qiongshan Cao and Zhongyang Guo and Yong Yang",

note = "Publisher Copyright: {\textcopyright} 2015 Taylor \& Francis Group, London.; Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014 ; Conference date: 27-12-2014 Through 28-12-2014",

year = "2015",

doi = "10.1201/b18508-64",

language = "英语",

isbn = "9781138028111",

series = "Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014",

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Cao, Q, Guo, Z & Yang, Y 2015, An improved back propagation neural network approach to the remote sensing land use and land cover classification. 在 A Hu (编辑), Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014. Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014, CRC Press/Balkema, 页码 369-373, Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014, Shanghai, 中国, 27/12/14. https://doi.org/10.1201/b18508-64

An improved back propagation neural network approach to the remote sensing land use and land cover classification. / Cao, Qiongshan; Guo, Zhongyang; Yang, Yong.
Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014. 编辑 / Ally Hu. CRC Press/Balkema, 2015. 页码 369-373 (Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - An improved back propagation neural network approach to the remote sensing land use and land cover classification

AU - Cao, Qiongshan

AU - Guo, Zhongyang

AU - Yang, Yong

PY - 2015

Y1 - 2015

N2 - Land Use and Land Cover (LULC) is always an essential research focus as it is of great use in many fields. Though more and more data with high resolution can be used in LULC classification as time goes by, the remote sensing data is always useful because it contains a lot of information and can offer an overview of the study area. Thus, it is still important to find good methods to use the remote sensing data in the LULC classification. In this paper, the Back Propagation (BP) neural network improved by the Simulated Annealing (SA) algorithm is used as an approach and the result turns out to be good. This method can effectively classify the surface features into different categories.

AB - Land Use and Land Cover (LULC) is always an essential research focus as it is of great use in many fields. Though more and more data with high resolution can be used in LULC classification as time goes by, the remote sensing data is always useful because it contains a lot of information and can offer an overview of the study area. Thus, it is still important to find good methods to use the remote sensing data in the LULC classification. In this paper, the Back Propagation (BP) neural network improved by the Simulated Annealing (SA) algorithm is used as an approach and the result turns out to be good. This method can effectively classify the surface features into different categories.

UR - https://www.scopus.com/pages/publications/84943614286

U2 - 10.1201/b18508-64

DO - 10.1201/b18508-64

M3 - 会议稿件

AN - SCOPUS:84943614286

SN - 9781138028111

T3 - Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014

SP - 369

EP - 373

BT - Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014

A2 - Hu, Ally

PB - CRC Press/Balkema

T2 - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014

Y2 - 27 December 2014 through 28 December 2014

ER -

Cao Q, Guo Z, Yang Y. An improved back propagation neural network approach to the remote sensing land use and land cover classification. 在 Hu A, 编辑, Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014. CRC Press/Balkema. 2015. 页码 369-373. (Computer Science and Applications - Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014). doi: 10.1201/b18508-64

An improved back propagation neural network approach to the remote sensing land use and land cover classification

摘要

出版系列

会议

联合国可持续发展目标

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