Optimal selection of remote sensing feature variables for land cover classification

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作者信息关键词期刊信息会议信息基础信息归属信息摘要

成果类型：

会议论文

作者：

Zeng, Wen*;Lin, Hui;Yan, Enping;Jiang, Qian;Lu, Hongwang;...

通讯作者：

Zeng, Wen

作者机构：

[Zeng, Wen; Yan, Enping; Jiang, Qian; Lin, Hui] Cent South Univ & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha, Hunan, Peoples R China.

[Zeng, Wen; Yan, Enping; Jiang, Qian; Lin, Hui] Key Lab Forestry Remote Sensing Based Big Data &, Changsha, Hunan, Peoples R China.

[Zeng, Wen; Yan, Enping; Jiang, Qian; Lin, Hui] Key Lab State Forestry Adm Forest Resources Manag, Changsha, Hunan, Peoples R China.

[Lu, Hongwang; Wu, Simin] Cent South Univ Forestry & Technol, Coll Forestry, Changsha, Hunan, Peoples R China.

通讯机构：

[Zeng, Wen] C

[Zeng, Wen] K

Cent South Univ & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha, Hunan, Peoples R China.

Key Lab Forestry Remote Sensing Based Big Data &, Changsha, Hunan, Peoples R China.

Key Lab State Forestry Adm Forest Resources Manag, Changsha, Hunan, Peoples R China.

语种：

英文

关键词：

Remote sensing;Land cover classification;Feature variables selection;JM distance;You County

期刊：

2018 FIFTH INTERNATIONAL WORKSHOP ON EARTH OBSERVATION AND REMOTE SENSING APPLICATIONS (EORSA)

ISSN：

2380-8039

年：

2018

页码：

246-250

会议名称：

5th International Workshop on Earth Observation and Remote Sensing Applications (EORSA)

会议论文集名称：

International Workshop on Earth Observation and Remote Sensing Applications

会议时间：

JUN 18-20, 2018

会议地点：

Xian, PEOPLES R CHINA

会议主办单位：

[Zeng, Wen;Lin, Hui;Yan, Enping;Jiang, Qian] Cent South Univ & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha, Hunan, Peoples R China.^[Zeng, Wen;Lin, Hui;Yan, Enping;Jiang, Qian] Key Lab Forestry Remote Sensing Based Big Data &, Changsha, Hunan, Peoples R China.^[Zeng, Wen;Lin, Hui;Yan, Enping;Jiang, Qian] Key Lab State Forestry Adm Forest Resources Manag, Changsha, Hunan, Peoples R China.^[Lu, Hongwang;Wu, Simin] Cent South Univ Forestry & Technol, Coll Forestry, Changsha, Hunan, Peoples R China.

会议赞助商：

Xian Univ Sci & Technol, Changan Univ, Cent S Univ Forestry & Technol, Indiana State Univ, IEEE Geoscience & Remote Sensing Soc, Int Soc Photogrammetry & Remote Sensing, Grp Earth Observat, Beijing PIESAT Informat Technol Co Ltd, State Key Lab Geo Informat Engn, Xian Reconnaissance Inst Surveying & Mapping, MDPI, Remote Sensing Journal, Taylor & Francis Grp, CRC Press

主编：

Weng, Q Gamba, P Chang, NB Wang, G Yao, W

出版地：

345 E 47TH ST, NEW YORK, NY 10017 USA

出版者：

IEEE

ISBN：

978-1-5386-6642-5

基金类别：

National Natural Science Foundation of China "The study on the selection of band windows of forest remote sensing monitoring" [31370639]

机构署名：

本校为其他机构

院系归属：

林学院

摘要：

With the abundance of spectral information and texture information of remote sensing images, the feature variables applicable to land cover classification also increase. But the high-dimensional features will deteriorate the classifier performance. In this paper, an improved selection method was proposed by considering separability and redundancy between feature variables. The study area - You County was divided into eight land cover types based on the SPOT-5 remote sensing image in 2009 combined with the continuous inventory data of forest resources during the same period. A total of 3,921 sa...

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Optimal selection of remote sensing feature variables for land cover classification

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