测绘通报 ›› 2024, Vol. 0 ›› Issue (7): 30-34.doi: 10.13474/j.cnki.11-2246.2024.0706

• 学术研究 • 上一篇    下一篇

利用高分七号影像的溢洪道自动识别

陈佳1, 张文1, 李俊杰1, 杨智文2, 孟令奎1, 李林宜1   

  1. 1. 武汉大学遥感信息工程学院, 湖北 武汉 430079;
    2. 黄河水利科学研究院, 河南 郑州 450000
  • 收稿日期:2023-10-27 发布日期:2024-08-02
  • 通讯作者: 李林宜。E-mail:lilinyi@whu.edu.cn
  • 作者简介:陈佳(2000—),女,硕士生,主要研究方向为水利遥感监测。E-mail:whuchenjia@whu.edu.cn
  • 基金资助:
    国家重点研发计划(2021YFB3900603)

Spillway automatic extraction based on GF-7 satellite data

CHEN Jia1, ZHANG Wen1, LI Junjie1, YANG Zhiwen2, MENG Lingkui1, LI Linyi1   

  1. 1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;
    2. Yellow River Institute of Hydraulic Research, Zhengzhou 450000, China
  • Received:2023-10-27 Published:2024-08-02

摘要: 溢洪道是重要的水工建筑物,利用遥感影像对其进行智能识别具有重要的科学研究意义和工程应用价值。溢洪道目标对象小、背景复杂,难以与一般建筑物区分,为基于遥感影像的溢洪道目标检测带来了极大的挑战。本文利用国产高分七号卫星影像亚米级空间分辨率优势及其立体成像能力,构建了溢洪道几何立体指数(SGSI)并提出了一种溢洪道自动识别方法。首先经过数据预处理获取融合影像和高精度DSM;然后采用均值漂移(Mean-Shift)算法对融合影像进行分割,提取出分割对象的多维特征并通过多特征联合决策确定目标对象;最后结合目标对象的空间上下文关系对溢洪道图斑进行整合和后处理,从而输出最终识别结果,实现对溢洪道的自动识别。本文选取3个含溢洪道的水库大坝对所提方法进行了验证。试验结果表明,该算法提取的溢洪道边界与参考边界匹配较好,整体精度可达89.23%,能够高效、准确地对溢洪道进行自动识别。

关键词: 高分七号卫星, 溢洪道几何立体指数, 自动识别, 面向对象, 多特征联合决策

Abstract: Spillways are important hydraulic structures. Intelligent detection of spillways in remote sensing images are of great scientific significance and application values. However, due to the small scale and intricate surroundings of spillways, it is difficult to automatically detect spillways in remote sensing images. In this study, the domestically developed high-resolution imagery from the GF-7 satellite is used, which has stereoscopic imaging capability. The Spillway Geometry Stereoscopic Index (SGSI) is constructed and an automatic spillway recognition method is proposed. Firstly, data preprocessing is conducted to obtain fused imagery and high-precision DSM. Next, the Mean-Shift algorithm is employed for segmenting the fused imagery, extracting multi-dimensional features of segmented objects, and determining target objects through multi-feature joint decision-making. Finally, integrated and post-processing of spillway patches are performed based on the spatial context relationship of target objects to output the final recognition results, achieves automatic recognition of spillways. The proposed method is validated on three dam reservoirs containing spillways, and experimental results show good matching between the extracted spillway boundaries and reference boundaries, with an overall accuracy of 89.23%. The proposed method is proved to be efficient and accurate for automatic spillway recognition.

Key words: GF-7 satellite, spillway geometric solid index, automatic recognition, object oriented, multi-feature joint decision-making

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