地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (11): 2234-2244.doi: 10.12082/dqxxkx.2022.220122

• 遥感科学与应用技术 • 上一篇    下一篇

基于等角边界距离相似性的遥感影像面状图斑匹配研究

陈军1,*(), 马世岩1, 陈玲2, 江明桦1   

  1. 1.成都信息工程大学 资源与环境学院,成都 610225
    2.新疆地矿局第一水文工程地质大队,乌鲁木齐 830091
  • 收稿日期:2022-03-16 修回日期:2022-05-20 出版日期:2022-11-25 发布日期:2023-01-25
  • 作者简介:陈 军(1979— ),男,四川南充人,副教授,主要从事地理信息与人工智能,地理信息系统开发与集成研究。E-mail: cj@cuit.edu.cn
  • 基金资助:
    四川省科技计划项目(2020YFG0146);国家重点研发计划项目(2018YFB0505300)

A Target Matching Method for Remote Sensing Image Patches based on Equiangular Boundary Distance Similarity

CHEN Jun1,*(), MA Shiyan1, CHEN Ling2, JIANG Minghua1   

  1. 1. Institute of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China
    2. Hydrogeology & Engineering Geology Exploration Team of XinJiang Geology & Mineral Bureau, Urumqi 830091, China
  • Received:2022-03-16 Revised:2022-05-20 Online:2022-11-25 Published:2023-01-25
  • Contact: CHEN Jun
  • Supported by:
    Sichuan Science and technology planning project(2020YFG0146);National Key Research and Development Program of China(2018YFB0505300)

摘要:

基于形态特征的目标匹配是地图空间认知、地表位置自动感知等领域的重要方法。然而,由于不同数据源的遥感影像提取的同一个空间目标在空间尺度和局部形态上存在一定的差异,现有形态匹配算法难以获得较高的匹配精度。本文以等角边界距离编码及相似性算法为核心,提出了面向遥感影像的面状图斑匹配方法。① 从面目标的质心以10°为间隔,顺时针方向从质心向外引36条射线,获取面目标的边缘点,形成等角边界距离编码;② 提出了与该编码相适应的几个综合形态指标,包括自相似度、圆形度、形态复杂度和质心偏移度,并建立了面状图斑相似度算法;在此基础上,结合近邻目标的形态相似度,构建了遥感影像面状图斑匹配算法;③ 从不同数据源的在线遥感影像上提取中国西部湖泊与全球湖泊,开展面状图斑匹配实验。通过实验,发现本文的面状图斑相似度算法相对于按距离采样的相似度算法,召回率提升了13.8%;在添加近邻目标相似度约束的基础上,面状图斑匹配精度达到90%以上。通过算法适应性分析,发现该算法在一定的投影变形下仍能保持一定的匹配精度;如果空间目标及其邻域目标的总体形态和分布接近,不同空间尺度的匹配精度保持在80%以上。本文的研究为地图空间认知、地表位置机器自动感知提供了新的思路和方法。

关键词: 遥感影像, 面状图斑, 形态特征, 面相似度, 目标匹配, 等角边界距离编码, 位置匹配与计算

Abstract:

Target matching based on morphological characteristics is a key technical method in the fields of spatial cognition of maps and automatic perception of surface locations. However, due to the differences in spatial scale and local morphology of spatial targets extracted from different remote sensing images, it is difficult to use existing shape matching algorithms to obtain high matching accuracy. In this study, based on the equiangular boundary distance coding and similarity algorithm, a method of matching remote sensing image patches is proposed. Specifically, first, from the centroid of a surface target, 36 lines are created in a clockwise direction with intervals (10°) to obtain the boundary points of the surface target, a process referred to as equiangular boundary distance coding. Then, several shape indices are proposed for the boundary points, including the self-similarity, circularity, shape complexity, and centroid offset, based on which a shape similarity algorithm for surface targets is established. Furthermore, based on the shape similarity of neighboring targets, a matching algorithm for remote sensing image patches is proposed. Finally, the lakes at the global scale and in the western China are extracted from online remote sensing images of different data sources to carry out the matching experiments. Based on the experiments, we found that the recall rate of the algorithm proposed in this paper is improved by 13.8% compared to the similarity algorithm sampled by distance. The precision reaches more than 90% when the similarity of the neighbor targets is considered. Through algorithm adaptation analysis, our proposed algorithm can still maintain a reasonable matching accuracy with certain projection deformation. If the overall morphology and spatial distribution of targets and their neighboring targets are similar, the matching accuracy of different spatial scales could be maintained above 80%. This paper provides new ideas and methods for spatial cognition of maps and automatic perception of surface locations.

Key words: remote sensing images, image patches, morphological character, polygon similarity, target Matching, equiangular boundary distance coding, location matching and calculation