SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS

DU Pei-jun; ZHOU Xing-dong; GUO Da-zhi

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

SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS

DU Pei-jun, ZHOU Xing-dong, GUO Da-zhi

文章导航 > Chinese Geographical Science > 2003 > 13(1): 79-83

DU Pei-jun, ZHOU Xing-dong, GUO Da-zhi. SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS[J]. 中国地理科学, 2003, 13(1): 79-83.

引用本文:

DU Pei-jun, ZHOU Xing-dong, GUO Da-zhi. SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS[J]. 中国地理科学, 2003, 13(1): 79-83.

DU Pei-jun, ZHOU Xing-dong, GUO Da-zhi. SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS[J]. Chinese Geographical Science, 2003, 13(1): 79-83.

Citation:

DU Pei-jun, ZHOU Xing-dong, GUO Da-zhi. SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS[J]. Chinese Geographical Science, 2003, 13(1): 79-83.

SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS

1.
China University of Mining & Technology, Xvzhou 221008, P. R. China;
2.
Shanghai Jiaotong University, Shanghai 200030 P. R. China;
3.
Xuzhou Normal University, Xuzhou 221011, P. R. China

基金项目: Under the auspices of the Research Foundation of Doctoral Point of China(No. RFDP20010290006).

详细信息

作者简介:
DU Pei-jun(1975- ), male, a native of Wutai County, Shaanxi Province, Ph.D., associate professor, specialized in theories and application of RS, GIS and their integration.

中图分类号: TP79

SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS

1.
China University of Mining & Technology, Xvzhou 221008, P. R. China;
2.
Shanghai Jiaotong University, Shanghai 200030 P. R. China;
3.
Xuzhou Normal University, Xuzhou 221011, P. R. China

摘要: In order to apply Satellite Remote Sensing(RS)to mining areas, some key issues should be solved. Based on an introduction to relative studying background, related key issues are proposed and analyzed oriented to the development of RS information science and demands of mining areas. Band selection and combination optimization of Landsat TM is discussed firstly, and it proved that the combination of Band 3, Band 4 and Band 5 has the largest information amount in all three-band combination schemes by both N-dimensional entropy method and Genetic Algorithm(GA). After that the filtering of Radarsat image is discussed. Different filtering methods are experimented and compared, and adaptive methods are more efficient than others. Finally the classification of satellite RS image is studied, and some new methods including classification by improved BPNN(Back Propagation Neural Network)and classification based on GIS and knowledge are proposed.
- Satellite Remote Sensing /
- mining areas /
- band combination /
- filtering /
- image classification
Abstract: In order to apply Satellite Remote Sensing(RS)to mining areas, some key issues should be solved. Based on an introduction to relative studying background, related key issues are proposed and analyzed oriented to the development of RS information science and demands of mining areas. Band selection and combination optimization of Landsat TM is discussed firstly, and it proved that the combination of Band 3, Band 4 and Band 5 has the largest information amount in all three-band combination schemes by both N-dimensional entropy method and Genetic Algorithm(GA). After that the filtering of Radarsat image is discussed. Different filtering methods are experimented and compared, and adaptive methods are more efficient than others. Finally the classification of satellite RS image is studied, and some new methods including classification by improved BPNN(Back Propagation Neural Network)and classification based on GIS and knowledge are proposed.
- Satellite Remote Sensing /
- mining areas /
- band combination /
- filtering /
- image classification

[1]	Kai LIU, Dapeng ZHANG, Tan CHEN, Peipei CUI, Chenyu FAN, Chunqiao SONG. Monitoring Surface Water Change in Northeast China in 1999–2020: Evidence from Satellite Observation and Refined Classification . Chinese Geographical Science, 2024, 34(1): 106-117. doi: 10.1007/s11769-024-1411-3
[2]	Xiaohong CHEN, Haihan LIU, Yongle AN, Yue DAI, Wei PAN, Ying WANG, Wei TANG. Spatiotemporal Differences and Classification Regulation of Urbanization Impact on Agricultural Development in Shrinking Areas: A Case Study of 15 Shrinking Cities in Three Provinces of Northeast China . Chinese Geographical Science, 2023, 33(6): 1053-1067. doi: 10.1007/s11769-023-1392-7
[3]	Xinshuang WANG, Jiancheng CAO, Jiange LIU, Xiangwu LI, Lu WANG, Feihang ZUO, Mu BAI. Improving the Interpretability and Reliability of Regional Land Cover Classification by U-Net Using Remote Sensing Data . Chinese Geographical Science, 2022, 32(6): 979-994. doi: 10.1007/s11769-022-1315-z
[4]	Hengyu GU, ROWE Francisco, Ye LIU, Tiyan SHEN. Geography of Talent in China During 2000–2015: An Eigenvector Spatial Filtering Negative Binomial Approach . Chinese Geographical Science, 2021, 31(2): 297-312. doi: 10.1007/s11769-021-1191-y
[5]	SUO Anning, XU Jingping, LI Xuchun, WEI Baoquan. Evaluation of Port Prosperity Based on High Spatial Resolution Satellite Remote Sensing Images . Chinese Geographical Science, 2020, 30(5): 889-899. doi: 10.1007/s11769-020-1153-9
[6]	LI Xianju, CHEN Gang, LIU Jingyi, CHEN Weitao, CHENG Xinwen, LIAO Yiwei. Effects of RapidEye Imagery's Red-edge Band and Vegetation Indices on Land Cover Classification in an Arid Region . Chinese Geographical Science, 2017, 27(5): 827-835. doi: 10.1007/s11769-017-0894-6
[7]	ZHENG Wei, SHAO Jiali, WANG Meng, HUANG Dapeng. A Thin Cloud Removal Method from Remote Sensing Image for Water Body Identification . Chinese Geographical Science, 2013, 23(4): 460-469. doi: 10.1007/s11769-013-0601-1
[8]	SUN Yonghua, GONG Huili, LI Xiaojuan, et al. Extracting Eco-hydrological Information of Inland Wetland from L-band Synthetic Aperture Radar Image in Honghe National Nature Reserve, Northeast China . Chinese Geographical Science, 2011, 21(2): 241-248.
[9]	GONG Huili, JIAO Cuicui, ZHOU Demin, LI Na. Scale Issues of Wetland Classification and Mapping Using Remote Sensing Images: A Case of Honghe National Nature Reserve in Sanjiang Plain, Northeast China . Chinese Geographical Science, 2011, 21(2): 230-240.
[10]	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 . Chinese Geographical Science, 2011, 21(6): 656-665.
[11]	WANG Ning, ZHANG Gang, LIU Te, CAI Qingxiang. Distribution of Methyl Mercury in Rana chensinensis and Environmental Media in Gold-mining Areas of Upper Reaches of Songhua River, China . Chinese Geographical Science, 2010, 20(4): 330-336. doi: 10.1007/s11769-010-0405-5
[12]	PENG Guangxiong, DENG Lei, CUI Weihong, MING Tao, SHEN Wei. Remote Sensing Monitoring of Tobacco Field Based on Phenological Characteristics and Time Series Image―A Case Study of Chengjiang County, Yunnan Province, China . Chinese Geographical Science, 2009, 19(2): 186-193. doi: 10.1007/s11769-009-0186-x
[13]	FU Xiaoyang, P E R Dale, ZHANG Shuqing. Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification . Chinese Geographical Science, 2008, 18(2): 162-170. doi: 10.1007/s11769-008-0162-x
[14]	LIU Yongxue, LI Manchun, MAO Liang, XU Feifei, HUANG Shuo. Review of Remotely Sensed Imagery Classification Patterns Based on Object-oriented Image Analysis . Chinese Geographical Science, 2006, 16(3): 282-288.
[15]	PANG Lei, ZHANG Ming-bo, ZHANG Ji-xian, ZHENG Zhao-qing, LIN Zong-jian. SHALLOW SEA WATER DEPTH RETRIEVAL BASED ON BOTTOM CLASSIFICATION FROM REMOTE SENSING IMAGERY . Chinese Geographical Science, 2004, 14(3): 258-262.
[16]	ZHANG Bao-guang. APPLICATION OF REMOTE SENSING TECHNOLOGY TO POPULATION ESTIMATION . Chinese Geographical Science, 2003, 13(3): 267-271.
[17]	ZHAO Shu-he, FENG Xue-zhi, KANG Guo-ding, RAMADAN Elnazir. MULTI-SOURCE REMOTE SENSING IMAGE FUSION BASED ON SUPPORT VECTOR MACHINE . Chinese Geographical Science, 2002, 12(3): 244-248.
[18]	ZHANG Shu-qing, ZHANG Shi-kui, ZHANG Jun-yan. A STUDY ON WETLAND CLASSIFICATION MODEL OF REMOTE SENSING IN THE SANGJIANG PLAIN . Chinese Geographical Science, 2000, 10(1): 68-73.
[19]	庄大方, 凌扬荣, Yoshio Awaya. INTEGRATED VEGETATION CLASSIFICATION AND MAPPING USING REMOTE SENSING AND GIS TECHNIQUES . Chinese Geographical Science, 1999, 9(1): 49-56.
[20]	刘兆礼, 黄铁青, 万恩璞, 张养贞. STUDY ON MODEL FOR REMOTE SENSING ESTIMATION OF MAIZE YIELD . Chinese Geographical Science, 1998, 8(2): 161-167.

点击查看大图

计量

文章访问数: 879
HTML全文浏览量: 24
PDF下载量: 943
被引次数: 0

SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS

1. China University of Mining & Technology, Xvzhou 221008, P. R. China;

2. Shanghai Jiaotong University, Shanghai 200030 P. R. China;

3. Xuzhou Normal University, Xuzhou 221011, P. R. China

基金项目: Under the auspices of the Research Foundation of Doctoral Point of China(No. RFDP20010290006).

作者简介:
DU Pei-jun(1975- ), male, a native of Wutai County, Shaanxi Province, Ph.D., associate professor, specialized in theories and application of RS, GIS and their integration.

收稿日期: 2002-09-28
刊出日期: 2003-03-20

中图分类号: TP79

关键词:

Satellite Remote Sensing /

mining areas /

band combination /

filtering /

image classification

摘要: In order to apply Satellite Remote Sensing(RS)to mining areas, some key issues should be solved. Based on an introduction to relative studying background, related key issues are proposed and analyzed oriented to the development of RS information science and demands of mining areas. Band selection and combination optimization of Landsat TM is discussed firstly, and it proved that the combination of Band 3, Band 4 and Band 5 has the largest information amount in all three-band combination schemes by both N-dimensional entropy method and Genetic Algorithm(GA). After that the filtering of Radarsat image is discussed. Different filtering methods are experimented and compared, and adaptive methods are more efficient than others. Finally the classification of satellite RS image is studied, and some new methods including classification by improved BPNN(Back Propagation Neural Network)and classification based on GIS and knowledge are proposed.

English Abstract

DU Pei-jun, ZHOU Xing-dong, GUO Da-zhi. SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS[J]. 中国地理科学, 2003, 13(1): 79-83.

引用本文:

DU Pei-jun, ZHOU Xing-dong, GUO Da-zhi. SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS[J]. 中国地理科学, 2003, 13(1): 79-83.

DU Pei-jun, ZHOU Xing-dong, GUO Da-zhi. SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS[J]. Chinese Geographical Science, 2003, 13(1): 79-83.

Citation:

DU Pei-jun, ZHOU Xing-dong, GUO Da-zhi. SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS[J]. Chinese Geographical Science, 2003, 13(1): 79-83.

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

SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS

作者简介: DU Pei-jun(1975- ), male, a native of Wutai County, Shaanxi Province, Ph.D., associate professor, specialized in theories and application of RS, GIS and their integration.

SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS

计量

出版历程

SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS

1. China University of Mining & Technology, Xvzhou 221008, P. R. China; 2. Shanghai Jiaotong University, Shanghai 200030 P. R. China; 3. Xuzhou Normal University, Xuzhou 221011, P. R. China

作者简介: DU Pei-jun(1975- ), male, a native of Wutai County, Shaanxi Province, Ph.D., associate professor, specialized in theories and application of RS, GIS and their integration.

English Abstract

SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS

1. China University of Mining & Technology, Xvzhou 221008, P. R. China; 2. Shanghai Jiaotong University, Shanghai 200030 P. R. China; 3. Xuzhou Normal University, Xuzhou 221011, P. R. China

全文HTML

目录

作者简介:
DU Pei-jun(1975- ), male, a native of Wutai County, Shaanxi Province, Ph.D., associate professor, specialized in theories and application of RS, GIS and their integration.

1. China University of Mining & Technology, Xvzhou 221008, P. R. China;

2. Shanghai Jiaotong University, Shanghai 200030 P. R. China;

3. Xuzhou Normal University, Xuzhou 221011, P. R. China

作者简介:
DU Pei-jun(1975- ), male, a native of Wutai County, Shaanxi Province, Ph.D., associate professor, specialized in theories and application of RS, GIS and their integration.

1. China University of Mining & Technology, Xvzhou 221008, P. R. China;

2. Shanghai Jiaotong University, Shanghai 200030 P. R. China;

3. Xuzhou Normal University, Xuzhou 221011, P. R. China