[1]孔美美,陈锻生.采用混合像元分解的水库面积提取及变化监测[J].华侨大学学报(自然科学版),2017,38(3):385-390.[doi:10.11830/ISSN.1000-5013.201703018]
 KONG Meimei,CHEN Duansheng.Reservoir Water Area Extraction and Monitoring Using Pixel Unmixing[J].Journal of Huaqiao University(Natural Science),2017,38(3):385-390.[doi:10.11830/ISSN.1000-5013.201703018]
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采用混合像元分解的水库面积提取及变化监测()
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《华侨大学学报(自然科学版)》[ISSN:1000-5013/CN:35-1079/N]

卷:
第38卷
期数:
2017年第3期
页码:
385-390
栏目:
出版日期:
2017-05-20

文章信息/Info

Title:
Reservoir Water Area Extraction and Monitoring Using Pixel Unmixing
文章编号:
1000-5013(2017)03-0385-06
作者:
孔美美 陈锻生
华侨大学 计算机科学与技术学院, 福建 厦门 361021
Author(s):
KONG Meimei CHEN Duansheng
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
关键词:
水体面积提取 混合像元分解 遥感 变化检测 环境监测 杏林湾水库
Keywords:
water area extraction pixel unmixing remote sensing change detection environment monitoring Xinglin Bay Reservoir
分类号:
TP391(257)
DOI:
10.11830/ISSN.1000-5013.201703018
文献标志码:
A
摘要:
利用Landsat-5 TM,Landsat-7 ETM+和Landsat-8 OLI遥感影像数据和高空间分辨率(1.14,0.54 m)的Google EarthTM地面实况图进行水库面积检测与变化监测的技术研究.采用完全约束最小二乘法混合像元分解(FCLS)方法提取不同水库水体信息,并与常用水体指数法及支持向量机(SVM)分类法进行比较,利用多时相FCLS方法监测厦门杏林湾水库2006-2014年间的面积动态变化.结果表明:基于FCLS的方法比NDWI等水体指数方法更能准确地获取水库水体面积大小及其变化信息.
Abstract:
We use Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI remote sensing images and high spatial resolution(1.14,0.54 m)Google EarthTM ground truth images to extract surface water and detect changes of reservoir area.Using fully constrained least squares method pixel unmixing(FCLS)method to extracting different reservoir surface water information and compared with water index method and support vector machine(SVM). Finally, using multi-temporal FCLS method to dynamic monitoring Xinglin Bay Reservoir water area in Xiamen between 2006 and 2014.The experimental results showed that: compared with NDWI and other indexes method, FCLS based method can obtain more accurate dynamic information of the reservoir surface water area.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2016-02-29
通信作者: 陈锻生(1959-),男,教授,博士,主要从事计算机视觉与多媒体技术的研究.E-mail:dschen@hqu.edu.cn.
基金项目: 国家自然科学基金面上资助项目(61370006); 福建省科技计划(工业引导性)重点项目(2015H0025)
更新日期/Last Update: 2017-05-20