Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area

DU Peijun; YUAN Linshan; XIA Junshi; et al.

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area

DU Peijun, YUAN Linshan, XIA Junshi, et al.

文章导航 > Chinese Geographical Science > 2011 > 21(6): 656-665

DU Peijun, YUAN Linshan, XIA Junshi, et al.. Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area[J]. 中国地理科学, 2011, 21(6): 656-665.

引用本文:

DU Peijun, YUAN Linshan, XIA Junshi, et al.. Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area[J]. Chinese Geographical Science, 2011, 21(6): 656-665.

Citation:

DU Peijun, YUAN Linshan, XIA Junshi, et al.. Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area[J]. Chinese Geographical Science, 2011, 21(6): 656-665.

Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area

(1. Department of Geoinformatics, Nanjing University, Nanjing 210093, China; 2. Key Laboratory for Land Environment and
Disaster Monitoring of SBSM, China University of Mining and Technology, Xuzhou 221116, China;
3.
East China Mineral Exploration and Development Bureau, Nanjing 210007, China)

Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area

(1. Department of Geoinformatics, Nanjing University, Nanjing 210093, China; 2. Key Laboratory for Land Environment and
Disaster Monitoring of SBSM, China University of Mining and Technology, Xuzhou 221116, China;
3.
East China Mineral Exploration and Development Bureau, Nanjing 210007, China)

摘要: In order to promote the application of Beijing-1 small satellite (BJ-1) remote sensing data, the multispectral and panchromatic
images captured by BJ-1 were used for land cover classification in Pangzhuang Coal Mining. An improved Intensity-Hue-Saturation (IHS)
fusion algorithm is proposed to fuse panchromatic and multispectral images, in which intensity component and panchromatic image are
combined using the weights determined by edge pixels in the panchromatic image identified by grey absolute correlation degree. This
improved IHS fusion algorithm outperforms traditional IHS fusion method to a certain extent, evidenced by its ability in preserving spectral
information and enhancing spatial details. Dempster-Shafer (D-S) evidence theory was adopted to combine the outputs of three member
classifiers to generate the final classification map with higher accuracy than that by any individual classifier. Based on this study, we
conclude that Beijing-1 small satellite remote sensing images are useful to monitor and analyze land cover change and ecological
environment degradation in mining areas, and the proposed fusion algorithms at data and decision levels can integrate the advantages of
multi-resolution images and multiple classifiers, improve the overall accuracy and produce a more reliable land cover map.
- grey absolute correlation degree /
- Intensity-Hue-Saturation (IHS) transformation /
- D-S evidence theory /
- Beijing-1 small satellite
Abstract: In order to promote the application of Beijing-1 small satellite (BJ-1) remote sensing data, the multispectral and panchromatic
images captured by BJ-1 were used for land cover classification in Pangzhuang Coal Mining. An improved Intensity-Hue-Saturation (IHS)
fusion algorithm is proposed to fuse panchromatic and multispectral images, in which intensity component and panchromatic image are
combined using the weights determined by edge pixels in the panchromatic image identified by grey absolute correlation degree. This
improved IHS fusion algorithm outperforms traditional IHS fusion method to a certain extent, evidenced by its ability in preserving spectral
information and enhancing spatial details. Dempster-Shafer (D-S) evidence theory was adopted to combine the outputs of three member
classifiers to generate the final classification map with higher accuracy than that by any individual classifier. Based on this study, we
conclude that Beijing-1 small satellite remote sensing images are useful to monitor and analyze land cover change and ecological
environment degradation in mining areas, and the proposed fusion algorithms at data and decision levels can integrate the advantages of
multi-resolution images and multiple classifiers, improve the overall accuracy and produce a more reliable land cover map.
- grey absolute correlation degree /
- Intensity-Hue-Saturation (IHS) transformation /
- D-S evidence theory /
- Beijing-1 small satellite

[1]	Yixu WANG, Shuo SUN, Wentian XU, Xuecheng FU, Lei YAO. Spatial Variability of PM_2.5 Pollution in Imbalanced Natural and Socioeconomic Processes: Evidence from the Beijing-Tianjin-Hebei Region of China . Chinese Geographical Science, 2023, 33(1): 161-174. doi: 10.1007/s11769-023-1331-7
[2]	He LI, Xiaoxuan LIANG. Responses of Housing Price under Different Directions of Population Change: Evidence from China’s Rust Belt . Chinese Geographical Science, 2022, 32(3): 405-417. doi: 10.1007/s11769-021-1248-y
[3]	Weichun HE, Erling LI, Zhizhen CUI. Evaluation and Influence Factor of Green Efficiency of China’s Agricultural Innovation from the Perspective of Technical Transformation . Chinese Geographical Science, 2021, 31(2): 313-328. doi: 10.1007/s11769-021-1192-x
[4]	Lu SUN, Jia WANG, Shuping CHANG. Population Spatial Distribution Based on Luojia 1-01 Nighttime Light Image: A Case Study of Beijing . Chinese Geographical Science, 2021, 31(6): 966-978. doi: 10.1007/s11769-021-1240-6
[5]	Nastaran NAJKAR, Mohammad Reza KOHANSAL, Mohammad GHORBANI. Impact of Industrial Agglomeration on Productivity: Evidence from Iran's Food Industry . Chinese Geographical Science, 2020, 30(2): 309-323. doi: 10.1007/s11769-019-1087-2
[6]	YAO Yangyang, ZHANG Guojun, XU Zhihua, BIAN Yan. Spatio-temporal Evolution of China's Economic Power Based on Asymmetric Theory . Chinese Geographical Science, 2020, 30(5): 765-775. doi: 10.1007/s11769-020-1148-6
[7]	DONG Lijing, WANG Yongchao, LIN Jiayi, ZHU Ermeng. The Community Renewal of Shantytown Transformation in Old Industrial Cities: Evidence from Tiexi Worker Village in Shenyang, China . Chinese Geographical Science, 2020, 30(6): 1022-1038. doi: 10.1007/s11769-020-1164-6
[8]	LUAN Qingzu, JIANG Wei, LIU Shuo, GUO Hongxiang. Impact of Urban 3D Morphology on Particulate Matter 2.5 (PM_2.5) Concentrations: Case Study of Beijing, China . Chinese Geographical Science, 2020, 30(2): 294-308. doi: 10.1007/s11769-020-1112-5
[9]	MA Shuang, ZENG Gang. Effects of Network Closure on Cooperative Innovation: Evidence from Dongying's Petroleum Equipment Industry in China . Chinese Geographical Science, 2019, 20(3): 517-527. doi: 10.1007/s11769-019-1046-y
[10]	PENG Jian, LIU Yanxu, SHEN Hong, XIE Pan, HU Xiaoxu, WANG Yanglin. Using Impervious Surfaces to Detect Urban Expansion in Beijing of China in 2000s . Chinese Geographical Science, 2016, 26(2): 229-243. doi: 10.1007/s11769-016-0802-5
[11]	SHENG Kerong, FAN Jie, SUN Wei, MA Hailong. From Sequential to Parallel Growth of Cities: Theory and Evidence from Canada . Chinese Geographical Science, 2016, 26(3): 377-388. doi: 10.1007/s11769-015-0750-5
[12]	HU Yecui, ZHENG Yunmei, ZHENG Xinqi. Simulation of Land-use Scenarios for Beijing Using CLUE-S and Markov Composite Models . Chinese Geographical Science, 2013, 23(1): 92-100.
[13]	HUANG Daquan, WAN Wei, DAI Teqi, LIANG Jinshe. Assessment of Industrial Land Use Intensity: A Case Study of Beijing Economic-technological Development Area . Chinese Geographical Science, 2011, 21(2): 222-229.
[14]	ZHOU Ji, ZHAN Wenfeng, HU Deyong, ZHAO Xiang. Improvement of Mono-window Algorithm for Retrieving Land Surface Temperature from HJ-1B Satellite Data . Chinese Geographical Science, 2010, 20(2): 123-131. doi: 10.1007/s11769-010-0123-z
[15]	XIANG Wei, WANG Yu, LI Ning, ZHU Qingwei. Grey-relation Analysis of Traffic System and Urbanization in Jilin Province of China . Chinese Geographical Science, 2007, 17(3): 216-221. doi: 10.1007/s11769-007-0216-5
[16]	LI Xiang, WANG Xinyuan, SHAO Wei, XIA Linyi, ZHANG Guangsheng, TIAN Bing, LI Wenda, PENG Peng. Forecast of Flood in Chaohu Lake Basin of China Based on Grey-Markov Theory . Chinese Geographical Science, 2007, 17(1): 64-68. doi: 10.1007/s11769-007-0064-3
[17]	SUN Tie-shan, LI Guo-ping, LU Ming-hua. STUDY ON BEIJING'S EMERGING MOBILE COMMUNICATION INDUSTRIAL CLUSTER AND ITS POLICY IMPLICATIONS . Chinese Geographical Science, 2003, 13(2): 104-111.
[18]	赵建. HYDROCHEMICAL INDEXES OF SEAWATER INTRUSION AND COMPREHENSIVE JUDGEMENT ON INTRUSIVE DEGREE . Chinese Geographical Science, 1999, 9(2): 151-158.
[19]	庄大方, 刘纪远. MODELING OF REGIONAL DIFFERENTIATION OFLAND-USE DEGREE IN CHINA . Chinese Geographical Science, 1997, 7(4): 302-309.
[20]	李家永, 李昌华. GRADATION AND ASSESSMENT OF DAMAGE DEGREE OF NATURAL LANDSCAPE IN XING, AN COUNTY, GUANGXI . Chinese Geographical Science, 1992, 2(4): 367-372.

点击查看大图

计量

文章访问数: 1387
HTML全文浏览量: 43
PDF下载量: 1472
被引次数: 0

Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area

(1. Department of Geoinformatics, Nanjing University, Nanjing 210093, China; 2. Key Laboratory for Land Environment and

Disaster Monitoring of SBSM, China University of Mining and Technology, Xuzhou 221116, China;

3. East China Mineral Exploration and Development Bureau, Nanjing 210007, China)

刊出日期: 2011-11-04

关键词:

grey absolute correlation degree /

Intensity-Hue-Saturation (IHS) transformation /

D-S evidence theory /

Beijing-1 small satellite

摘要: In order to promote the application of Beijing-1 small satellite (BJ-1) remote sensing data, the multispectral and panchromatic
images captured by BJ-1 were used for land cover classification in Pangzhuang Coal Mining. An improved Intensity-Hue-Saturation (IHS)
fusion algorithm is proposed to fuse panchromatic and multispectral images, in which intensity component and panchromatic image are
combined using the weights determined by edge pixels in the panchromatic image identified by grey absolute correlation degree. This
improved IHS fusion algorithm outperforms traditional IHS fusion method to a certain extent, evidenced by its ability in preserving spectral
information and enhancing spatial details. Dempster-Shafer (D-S) evidence theory was adopted to combine the outputs of three member
classifiers to generate the final classification map with higher accuracy than that by any individual classifier. Based on this study, we
conclude that Beijing-1 small satellite remote sensing images are useful to monitor and analyze land cover change and ecological
environment degradation in mining areas, and the proposed fusion algorithms at data and decision levels can integrate the advantages of
multi-resolution images and multiple classifiers, improve the overall accuracy and produce a more reliable land cover map.

English Abstract

Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area

(1. Department of Geoinformatics, Nanjing University, Nanjing 210093, China; 2. Key Laboratory for Land Environment and

Disaster Monitoring of SBSM, China University of Mining and Technology, Xuzhou 221116, China;

3. East China Mineral Exploration and Development Bureau, Nanjing 210007, China)

Publish Date: 2011-11-04

Keywords:

grey absolute correlation degree /
Intensity-Hue-Saturation (IHS) transformation /
D-S evidence theory /
Beijing-1 small satellite

Abstract: In order to promote the application of Beijing-1 small satellite (BJ-1) remote sensing data, the multispectral and panchromatic
images captured by BJ-1 were used for land cover classification in Pangzhuang Coal Mining. An improved Intensity-Hue-Saturation (IHS)
fusion algorithm is proposed to fuse panchromatic and multispectral images, in which intensity component and panchromatic image are
combined using the weights determined by edge pixels in the panchromatic image identified by grey absolute correlation degree. This
improved IHS fusion algorithm outperforms traditional IHS fusion method to a certain extent, evidenced by its ability in preserving spectral
information and enhancing spatial details. Dempster-Shafer (D-S) evidence theory was adopted to combine the outputs of three member
classifiers to generate the final classification map with higher accuracy than that by any individual classifier. Based on this study, we
conclude that Beijing-1 small satellite remote sensing images are useful to monitor and analyze land cover change and ecological
environment degradation in mining areas, and the proposed fusion algorithms at data and decision levels can integrate the advantages of
multi-resolution images and multiple classifiers, improve the overall accuracy and produce a more reliable land cover map.

引用本文:

Citation:

全文HTML

下载: 全尺寸图片幻灯片

返回文章

用微信扫码二维码

分享至好友和朋友圈

留言板

Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area

Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area

计量

出版历程

Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area

(1. Department of Geoinformatics, Nanjing University, Nanjing 210093, China; 2. Key Laboratory for Land Environment and Disaster Monitoring of SBSM, China University of Mining and Technology, Xuzhou 221116, China; 3. East China Mineral Exploration and Development Bureau, Nanjing 210007, China)

English Abstract

Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area

(1. Department of Geoinformatics, Nanjing University, Nanjing 210093, China; 2. Key Laboratory for Land Environment and Disaster Monitoring of SBSM, China University of Mining and Technology, Xuzhou 221116, China; 3. East China Mineral Exploration and Development Bureau, Nanjing 210007, China)

全文HTML

目录

(1. Department of Geoinformatics, Nanjing University, Nanjing 210093, China; 2. Key Laboratory for Land Environment and

Disaster Monitoring of SBSM, China University of Mining and Technology, Xuzhou 221116, China;

3. East China Mineral Exploration and Development Bureau, Nanjing 210007, China)

(1. Department of Geoinformatics, Nanjing University, Nanjing 210093, China; 2. Key Laboratory for Land Environment and

Disaster Monitoring of SBSM, China University of Mining and Technology, Xuzhou 221116, China;

3. East China Mineral Exploration and Development Bureau, Nanjing 210007, China)