文献类型：期刊文献

中文题名：基于ZY-3遥感影像的不同地貌水边线提取方法

英文题名：Study on the Extraction of Waterline with Different Landforms Based on ZY-3 Remote Sensing Images

作者：董昭顷[1];付东洋[1];刘大召[1];余果[1];张小龙[1]

机构：[1]广东海洋大学电子与信息工程学院

年份：2019

卷号：39

期号：2

起止页码：34

中文期刊名：海洋测绘

外文期刊名：Hydrographic Surveying and Charting

收录：CSTPCD、、CSCD_E2019_2020、CSCD

基金：国家海洋公益专项(201305019);广东省自然科学基金(2014A030313603);广东省科技计划项目(2013B030200002;2016A020222016);广东海洋大学创新强校项目(GDOU2014050226;GDOU2014050246);广东海洋大学大学生创新创业训练计划项目(CXXL2017026);广东海洋大学博士科研启动项目(E11097);"海之帆"起航计划大学生科技创新培育项目(qhjh2017zr15);广东省哲学社会科学规划项目(GD12YGL04);广东省普通高校优秀青年创新人才培养计划项目(2012WYM_0077)

语种：中文

中文关键词：ZY-3卫星;近海水边线提取;阈值分割;神经元网络分类;面向对象

外文关键词：ZY-3 satellite;offshore waterline extraction;threshold segmentation;neural network classification;object orientation

中文摘要：水边线的精确提取对于沿海地区的经济开发和海域的使用管理具有重要意义。以雷州半岛东北部为研究区域,利用2017年资源三号(ZY-3)卫星数据为数据源,基于不同海岸地貌特征为划分依据,运用阈值分割法、神经元网络分类法和面向对象法对多光谱数据的人工海岸、砂质海岸、淤泥质海岸和红树林海岸进行水边线提取。通过目视解译提取融合图像的海岸线为基线,将提取的水边线与基线进行定性、定量分析。研究结果表明,对于人工岸线,神经元网络分类法最优,均方根误差为6.4m;对于砂质岸线,阈值分割法最优,均方根误差为5.4m;对于淤泥质及红树林岸线,面向对象法最优,均方根误差分别为23.3m和15.2m。该研究对于不同岸线的提取具有重要的借鉴和指导意义。

外文摘要：Accurate extraction of waterlines is significant to the economic development of coastal areas and the management of sea areas. The northeastern of Leizhou Peninsula is taken as the research area, and the satellite data of 2017 (ZY-3) is used as the data source.Based on the geomorphological features of different coasts,the threshold segmentation method, neural network classification method, and object-oriented classification method are used to extract the artificial coastlines, sandy coastlines, silty coastlines, and mangrove coastlines with multispectral data.The coastlines extracted from fusion image by manual visual interpretation as the baseline, the extracted waterlines and baselines are qualitatively and quantitatively analyzed. The experimental results show that for the artificial shoreline, the neural network classification method is optimal with the root mean square error is 6.4m;for the sandy shoreline, the threshold segmentation method is optimal with the root mean square error is 5.4m;for muddy and mangrove shorelines,the object-oriented classification method is optimal with root mean square errors of 23.3m and 15.2m, respectively.This study has important reference and guiding significance for extraction of different shorelines.