结合特征信息聚类分区的遥感影像配准方法

doi:10.7523/j.ucas.2022.021

摘要/Abstract

摘要： 针对由于地形起伏、地物类型丰富等因素导致一般配准方法无法正确拟合遥感影像局部区域的问题，提出一种基于特征信息层次聚类对影像区域快速划分实现精细拟合的方法。该方法利用差分空间尺度约束提取更高精度的SIFT的特征点，并结合Hellinger变换优化匹配效率，完成特征粗匹配。根据点邻域信息完成初始聚类，得到变换模型；计算匹配点对不同变换模型的符合程度构建倾向集，根据距离合并集合得到聚类中心，使用泰森多边形法生成子区域。求解每个子区域的变换模型并插值拼接，得到配准结果。使用农田、山地、沿海城镇地形的遥感影像进行实验，将SIFT+ST、FSC-SIFT、PSO-SIFT方法的配准效果与该方法进行对比，结果表明该方法的精度与目视配准效果均更优。

关键词: 遥感影像配准, 层次聚类, 局部拟合, 子区域划分, 模型一致性

Abstract: Aiming at the problem that the global registration model can’t correctly fit the local region due to topographic relief and rich ground object types, this paper proposes a method to quickly divide the image region and realize fine fitting based on feature information hierarchical clustering method. This method uses the scale constraint of difference space to extract the feature points of sift with higher accuracy, and optimizes the matching efficiency combined with Hellinger transform to complete the rough feature matching. The initial clustering is completed according to the point neighborhood information, and different models are obtained; the coincidence degree of matching points to different transformation models is calculated, the tendency set is constructed, the set is merged according to the distance to obtain the cluster center; and the sub region is generated using Tyson polygon method. The transformation model of each sub region is solved and interpolated to obtain the registration results. The remote sensing images of farmland, mountainous areas and coastal cities and towns are used for experiments. The registration effects of SIFT+ST, FSC-SIFT and PSO-SIFT methods are compared with this method. The results show that the accuracy and visual registration effect of this method are better.

Key words: remote sensing image registration, hierarchical clustering, partial fitting, sub-regional division, model consistency

中图分类号:

TP751

石正一, 刘朔, 夏昊. 结合特征信息聚类分区的遥感影像配准方法[J]. 中国科学院大学学报, 2024, 41(1): 97-106.

SHI Zhengyi, LIU Shuo, XIA Hao. A remote sensing image registration method combining feature information clustering and partitioning[J]. Journal of University of Chinese Academy of Sciences, 2024, 41(1): 97-106.

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