湖北农业科学 ›› 2021, Vol. 60 ›› Issue (5): 131-137.doi: 10.14088/j.cnki.issn0439-8114.2021.05.026

• 信息工程 • 上一篇    下一篇

基于GF-1号遥感影像的微小水体信息提取研究--以岔口流域为例

郑心玥, 郭青霞   

  1. 山西农业大学资源环境学院,山西 太谷 030801
  • 收稿日期:2020-09-30 出版日期:2021-03-10 发布日期:2021-03-22
  • 通讯作者: 郭青霞(1969-),教授,博士,(电子信箱)gqx696@163.com。
  • 作者简介:郑心玥(1996-),女,山东德州人,在读硕士研究生,研究方向为资源环境遥感,(电话)17835422596(电子信箱)zxy96win@163.com;
  • 基金资助:
    国家自然科学基金项目(41071345)

Research on information extraction of small water body based on GF-1 remote sensing image ——Take Chakou watershed as an example

ZHENG Xin-yue, GUO Qing-xia   

  1. College of Resources and Environment, Shanxi Agricultural University, Taigu 030801, Shanxi,China
  • Received:2020-09-30 Online:2021-03-10 Published:2021-03-22

摘要: 针对不同因素影响,山区微小水体提取效果不佳的问题,选用2016年9月1日GF-1号卫星遥感影像,运用NDWI、SWI决策树、SVM分类3种不同方法对位于黄土高原沟壑区的山西省岔口流域的微小水体进行了提取,并对提取效果进行视觉对比与精度验证。结果表明,相比中低分辨率遥感影像,高分辨率遥感影像对于山区微小水体的提取结果更好,精度更高,可运用GF-1号影像进行流域水体的监测、提取;影响流域水体提取的主要因素是亮色地物(主要为建筑物)和阴影;NDWI、SWI决策树、SVM分类3种方法中,NDWI方式提取的水体信息较弱,SWI决策树与SVM分类法精度较高,但SWI决策树法消除了建筑物、亮色地物的影响,并较明显地区分了阴影与水体,因此更适用于流域微小水体的提取。

关键词: 微小水体提取, GF-1卫星, 高分辨率影像, 岔口流域

Abstract: In response to the problem of poor extraction of tiny water bodies in mountainous areas due to different factors, the GF-1 satellite remote sensing image on September 1, 2016 was selected, and three different methods of NDWI, SWI decision tree, and SVM classification were used to were extracted the small water bodies in the Chakou basin of Shanxi province in the gully area, and the extraction effect was visually compared and accuracy verified. The results showed that the high-resolution remote sensing images have better extraction results and higher accuracy,for the small water bodies in mountainous areas than the low- and medium-resolution remote sensing images. GF-1 images can be used to monitor the water body of the river basin; The main factors affecting the water body extraction of the river basin are bright-colored ground objects (mainly buildings) and shadows; NDWI, SWI decision tree, SVM classification method among the three methods, the water body information extracted by the NDWI method is weaker, and the SWI decision tree and SVM classification methods have higher accuracy, but the SWI decision tree method eliminates the influence of buildings and bright-colored ground objects, and clearly distinguishes shadows and water bodies. So it is more suitable for the extraction of tiny water bodies in the watershed.

Key words: extraction of tiny water bodies, GF-1 satellite, high-resolution images, Chakou watershed

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