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国土资源遥感  2012, Vol. 24 Issue (3): 1-5    DOI: 10.6046/gtzyyg.2012.03.01
  综述 本期目录 | 过刊浏览 | 高级检索 |
水产养殖区域的遥感识别方法进展
程田飞1,2, 周为峰1, 樊伟1
1. 中国水产科学研究院渔业资源遥感信息技术重点开放实验室, 中国水产科学研究院 东海水产研究所, 上海 200090;
2. 上海海洋大学海洋科学学院, 上海 201306
Progress in the Methods for Extracting Aquaculture Areas from Remote Sensing Data
CHENG Tian-fei1,2, ZHOU Wei-feng1, FAN Wei1
1. Key and Open Laboratory of Fisheries Resources Remote Sensing and Information Technology, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China;
2. College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
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摘要 在分析遥感数据源的基础上,将水产养殖区域的识别方法归纳为目视解译、比值指数分析、对应分析、空间结构分析和面向对象提取等5大类,并综述了各种方法的优缺点和应用情况;最后对其研究方向和发展趋势进行了展望,指出应用高光谱遥感数据,利用人工智能、非线性理论以及专家系统的信息提取方法是其发展方向。
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刘文兰
张微
关键词 断裂构造蚀变异常图像增强找矿预测老挝    
Abstract:Based on an analysis of remote sensing data sources, the authors classify the methods into five categories, i.e., visual interpretation, ratio of index analysis, correspondence analysis, spatial structure analysis, and object-oriented information extraction, and summarize their characteristics and application fields. The research direction and development trends are also predicted, which include such application fields as hyper-spectral remote sensing data, artificial intelligence, expert systems and non-linear theory.
Key wordsfault    alteration anomaly    image enhancement    prediction of mineral prospecting    Laos
收稿日期: 2011-10-20      出版日期: 2012-08-20
:  TP751  
基金资助:中国水产科学研究院基本科研业务费(编号: 2012A1201); 中央级公益性科研院所基本科研业务费专项资金(中国水产科学研究院东海水产研究所)(编号: 2011T10, 2009T08)以及国家863计划项目(编号: 2003AA637030)共同资助。
通讯作者: 周为峰(1978-),女,副研究员。E-mail: zhwfzhwf@163.com
引用本文:   
程田飞, 周为峰, 樊伟. 水产养殖区域的遥感识别方法进展[J]. 国土资源遥感, 2012, 24(3): 1-5.
CHENG Tian-fei, ZHOU Wei-feng, FAN Wei. Progress in the Methods for Extracting Aquaculture Areas from Remote Sensing Data. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(3): 1-5.
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https://www.gtzyyg.com/CN/10.6046/gtzyyg.2012.03.01      或      https://www.gtzyyg.com/CN/Y2012/V24/I3/1
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