.png){kind=icon}
返回
An Efficient Algorithm for Depression Filling and Flat-Surface Processing in Raster DEMs
.png){kind=icon}
PRE
AI
DOI：10.1109/LGRS.2014.2324616.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Depressions (or pits) and flat surfaces (or flats) are general types of terrain in raster digital elevation models (DEMs). Depressions are lower areas surrounded by terrain without outlets, and flat surfaces are areas with no local gradient. To extract hydrologic or geomorphic properties from DEMs, these two types of terrain need to be addressed. This letter presents an efficient algorithm for filling depressions and for adding increments to flat surfaces. The algorithm builds on previous work, offering several important improvements. The improved algorithm uses two queues: a priority queue and a first-in, first-out (FIFO) queue. The FIFO queue is used to process depressions and flat surfaces, and the priority queue processes other terrain. The improved algorithm achieves an O(M log(2) M) time complexity, where M is less than the total number of cells, which is more efficient than the algorithm proposed by Wang and Liu. In addition, the improved algorithm not only fills depressions but also elevates flat surfaces for the convenience of extracting flow directions. Furthermore, to adapt to different data types, for example, integer, single-precision floating point, and double precision, the improved algorithm does not alter flat-surface elevations in DEMs directly but uses a mask matrix to mark the incremental elevation values of flat surfaces. In speed comparison testing, the improved algorithm performed up to 16%-32% faster than the original.
Keyword:
Depressions
digital elevation models (DEMs)
flat surfaces
geographic information system (GIS)
hydrology
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
机构
引用论文
A fast, simple and versatile algorithm to fill the depressions of digital elevation models.png)
CATENA
CATENAIF5.7
PREAI
分享收藏Area Partitioning for Channel Network Extraction Using Digital Elevation Models and Remote Sensing
PREAI
分享收藏
分享收藏
没有更多内容
没有更多内容