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2023 , Vol. 63 >Issue 1: 134 - 145

DOI: https://doi.org/10.16511/j.cnki.qhdxxb.2022.22.043

水利水电工程

基于多源国产高分辨率遥感影像的山区河流信息自动提取

  • 薛源 ,
  • 覃超 ,
  • 吴保生 ,
  • 李丹 ,
  • 傅旭东
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  • 1. 清华大学 水沙科学与水利水电工程国家重点实验室, 北京 100084;
    2. 中国煤炭科工集团有限公司 煤炭科学研究总院有限公司, 应急科学研究院, 北京 100013

收稿日期: 2022-04-05

  网络出版日期: 2023-01-11

基金资助

吴保生,教授,E-mail:baosheng@tsinghua.edu.cn

收起

Automatic extraction of mountain river information from multiple Chinese high-resolution remote sensing satellite images

  • XUE Yuan ,
  • QIN Chao ,
  • WU Baosheng ,
  • LI Dan ,
  • FU Xudong
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  • 1. State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China;
    2. Emergency Science Research Academy, China Coal Research Institute, China Coal Technology & Engineering Group Co., Ltd., Beijing 100013, China

Received date: 2022-04-05

  Online published: 2023-01-11

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摘要

高分辨率遥感影像地理信息是研究山区河流的重要数据源。针对山区地物类型复杂、河道狭窄等原因导致的河流表面信息提取完整性差、河宽难以自动提取且精度低等问题,结合随机森林(RF)和神经网络(ANN)算法,建立了河流表面信息提取方法RF-ANN。该方法支持并行运算且能降低热红外数据尺度辅助去噪,实现了对河流表面信息的像素级提取。利用Laplace算子及边缘算法改进了RivWidthCloud河宽提取算法,使其不需要人工设定判别阈值,提升了算法的普适性。以国产GF-1、ZY-3卫星影像为主要数据源,选取黄河一级支流皇甫川为研究区域。利用所建立的方法提取了皇甫川流域2级及以上河流的河流表面信息和平滩河宽。结果表明： RF-ANN的河流表面信息提取精度达到94.7%。提取河宽的平均误差为1.07 m (约0.5个像素),提取的最小有效河宽为6.1 m (约3个像素),提取河宽与检验河宽的R2和RMSE分别为0.93和1.52,宽度小于10 m极细河流、10~30 m细小河流、30~90 m较细河流及90 m以上较宽河流的河宽提取误差分别为18.5%、8.8%、2.0%和0.7%。该研究结果为山区河流几何形态特征提取及河流地貌空间分布研究提供了方法和数据支持。

关键词： 国产高分辨率卫星; 山区河流表面信息; 平滩河宽; 自动提取

本文引用格式

薛源 , 覃超 , 吴保生 , 李丹 , 傅旭东 . 基于多源国产高分辨率遥感影像的山区河流信息自动提取[J]. 清华大学学报（自然科学版）, 2023 , 63(1) : 134 -145 . DOI: 10.16511/j.cnki.qhdxxb.2022.22.043

Abstract

High-resolution geomorphic information from remote sensing images is a key part of mountain river research. However, the complete information about narrow rivers is difficult to extract automatically and accurately from complex backgrounds, especially with mountain shadows. This research uses a random forest (RF) algorithm with an artificial neural network (ANN), RF-ANN, to analyze remote sensing images. This method supports parallel operations and reduces the scale of the infrared data for noise removal to achieve pixel-level extraction of the river surfaces. The RivWidthCloud (RWC) method is improved using Laplacian and edge algorithms for automatic extraction of the bankfull river widths. The improved RWC method is generalizable since it does not require setting the discriminant threshold manually. The method is then applied to the Huangfuchuan River Basin on the Loess Plateau, China using images from the Chinese GF-1 and ZY-3 satellites as the primary data source to extract the river surfaces and widths of the rivers above level 2. The results show that the RF-ANN method has a 94.7% accuracy for extracting river surfaces. The bankfull river width extraction error is 1.07 m (about 0.5 pixels) and the minimum river width extracted by these methods is 6.1 m (about 3 pixels). R2 is 0.93 and the root men square errors (RMSE) is 1.52 for fitting the extracted river widths and the test river widths. For small rivers narrower than 10 m, the extraction error is 18.5%, for widths from 10 to 30 m the error is 8.8%, for widths from 30 to 90 m the error is 2.0%, and for rivers wider than 90 m the error is 0.7%. These results provide accurate datasets for watershed topography research in mountainous and other complex topographic regions.

Key words： Chinese high-resolution satellites; mountain river surface information; bankfull river width; automatic extraction

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