水边界信息是重要的地理专题信息,从遥感影像数据源中提取、识别出的水体要素信息被广泛应用于测绘、水利等诸多领域,常作为水陆一体化DEM快速构建的重要地理特征线。以汉江中下游某水域复杂河段为实例,基于无人机低空摄影测量获取的高...水边界信息是重要的地理专题信息,从遥感影像数据源中提取、识别出的水体要素信息被广泛应用于测绘、水利等诸多领域,常作为水陆一体化DEM快速构建的重要地理特征线。以汉江中下游某水域复杂河段为实例,基于无人机低空摄影测量获取的高分辨率正射影像,研究面向对象分类方法,提出了综合优化的多种可见光植被指数组合方法,用于高分辨影像的复杂水边界自动分类提取,并采用空间合并以及数据类型转换的方法,进行复杂水体边界线的制作。Water boundary information is an important geographical thematic information. Water element infor-mation extracted and identified from remote sensing image data sources is widely used in mapping, water conservancy and other fields, and is often used as an important geographical feature line for the rapid construction of land-water integrated DEM. Taking a complex river section in the middle and lower reaches of the Han River as an example, this paper studies object-oriented classification methods based on high-resolution orthophoto images obtained by low-altitude photogrammetry of unmanned aerial vehicle (UAV), and proposes a comprehensive optimization of multiple visible vegetation index combination methods for automatic classification and extraction of complex water boundaries in high-resolution images, and adopts spatial merging and data type conversion methods to make complex water boundary.展开更多
文摘水边界信息是重要的地理专题信息,从遥感影像数据源中提取、识别出的水体要素信息被广泛应用于测绘、水利等诸多领域,常作为水陆一体化DEM快速构建的重要地理特征线。以汉江中下游某水域复杂河段为实例,基于无人机低空摄影测量获取的高分辨率正射影像,研究面向对象分类方法,提出了综合优化的多种可见光植被指数组合方法,用于高分辨影像的复杂水边界自动分类提取,并采用空间合并以及数据类型转换的方法,进行复杂水体边界线的制作。Water boundary information is an important geographical thematic information. Water element infor-mation extracted and identified from remote sensing image data sources is widely used in mapping, water conservancy and other fields, and is often used as an important geographical feature line for the rapid construction of land-water integrated DEM. Taking a complex river section in the middle and lower reaches of the Han River as an example, this paper studies object-oriented classification methods based on high-resolution orthophoto images obtained by low-altitude photogrammetry of unmanned aerial vehicle (UAV), and proposes a comprehensive optimization of multiple visible vegetation index combination methods for automatic classification and extraction of complex water boundaries in high-resolution images, and adopts spatial merging and data type conversion methods to make complex water boundary.
