DEM辅助下的河道细小线性水体自适应迭代提取

River Linear Water Adaptive Auto-extraction on Remote Sensing Image Aided by DEM

提出一种河道细小线性水体自动识别方法。首先,利用全球30 m ASTER DEM数据,生成流域的水系分布矢量图;然后,以DEM水系分布图作为先验知识,通过空间分析形成信息提取目标区,为后续信息提取提供目标靶区;再次,通过水体光谱指数计算和NDWI全局阈值分割,得到靶区水体分布的初步信息;最后,在水体指数全域分割的基础上,通过局部水体指数物理特征分析、自适应阈值选择和迭代计算,实现局部河道水体精确提取。试验采用ETM+数据对伊犁河上游的支流河道进行信息提取,结果表明,该方法能够快速准确地完成大流域范围内的河道水系制图,并能够最大限度地降低细小河道水体识别中背景光谱信息混杂的干扰,提高遥感信息提取的针对性和计算的效率。

A hybrid approach for small linear river-water extraction by combining spatial information,spectral computation and global local iterative algorithm was explored.The process comprises four steps.Firstly,the river distribution maps which can be served as prior knowledge were derived from DEM data.Then a specific target area was identified using spatial analysis technique associated with river distribution mentioned above,furtherly we can get the preliminary water distribution information after applying water spectral index computation and global threshold segmentation.Finally,the precise extraction of river water can be achieved by the combination of local water index analysis,self-adaptive threshold selection and iterative computation.In this study,we adopted this approach to extract river-water information with ETM＋ data covering the middle reaches of Yili river,the results show that the proposed method can accomplish river network mapping effectively and precisely.Furthermore,it increased the direction of information extraction and computational efficiency by minimizing the influence of background spectral interference to the extreme extent.