Aerospace Heterogeneous Image Matching Algorithm Based on Improved SuperPoint

LIU Geng-chen; JIANG Liang; WU Guo-qiang; HUANG Kun

doi:10.12263/DZXB.20240724

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Aerospace Heterogeneous Image Matching Algorithm Based on Improved SuperPoint

PAPERS | 更新时间：2025-07-24

- Aerospace Heterogeneous Image Matching Algorithm Based on Improved SuperPoint
- ACTA ELECTRONICA SINICA Vol. 53, Issue 4, Pages: 1201-1211(2025)
- 作者机构：
  
  1.航天时代飞鸿技术有限公司，北京 100094
  2.中国航天科技集团有限公司智能无人系统总体技术研发中心，北京 100094
  3.中国航天科技集团有限公司第九研究院，北京 100094
  4.北京理工大学机电学院，北京 100081
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20240724
  CLC： TN91;
- Received：01 August 2024，
  
  Revised：2025-04-30，
  
  Published：25 April 2025
- 稿件说明：
移动端阅览
刘庚辰, 姜梁, 吴国强, 等. 基于改进SuperPoint的空天异源图像匹配算法[J]. 电子学报, 2025, 53(04): 1201-1211.

LIU Geng-chen, JIANG Liang, WU Guo-qiang, et al. Aerospace Heterogeneous Image Matching Algorithm Based on Improved SuperPoint[J]. Acta Electronica Sinica, 2025, 53(04): 1201-1211.
刘庚辰, 姜梁, 吴国强, 等. 基于改进SuperPoint的空天异源图像匹配算法[J]. 电子学报, 2025, 53(04): 1201-1211. DOI：10.12263/DZXB.20240724

LIU Geng-chen, JIANG Liang, WU Guo-qiang, et al. Aerospace Heterogeneous Image Matching Algorithm Based on Improved SuperPoint[J]. Acta Electronica Sinica, 2025, 53(04): 1201-1211. DOI：10.12263/DZXB.20240724

摘要

空天异源图像特征提取难度较大，图像匹配精度较低，给无人机精确目标定位带来了不利影响.SuperPoint-SuperGlue算法由于其自监督，易训练，精度高等特性，近年来在图像匹配领域应用较为广泛，但针对空天异源图像匹配领域，SuperPoint特征提取能力仍有待提高.为提高空天异源图像匹配精度，本文提出基于改进SuperPoint的空天异源图像匹配算法.首先，将群智能增强模块（Spatial Group-wise Enhance，SGE）与全局注意力机制（Global Attention Mechanism，GAM）引入到SuperPoint编码器中构成补充编码器，一定程度上解决了图像特征分布不均匀以及弱纹理图像特征提取较难的问题；其次，为进一步增强算法特征提取能力，将补充编码器与原SuperPoint编码器进行并联构成组合编码器，结合二者优势，提取差异性更大的图像特征，减少对相似区域的特征点误匹配，提高空天异源图像的匹配精度；最后，通过实验验证，在UAV-VisLoc 数据集上80像素误差区间以内可匹配图像数量可达82.14%，与原SuperPoint算法相比，80像素误差区间以内可匹配图像数量提高了6.05%，与其他先进算法相比，在各像素误差区间以内可匹配图像数量均有提高.实验表明，本文提出的算法可以有效地解决空天异源图像匹配中特征提取能力较弱，特征分布不均匀等问题.

Abstract

It is quite difficult to extract features from heterogeneous aerospace images

and the image matching accuracy is relatively low

which has a negative impact on the precise target positioning of unmanned aerial vehicles (UAVs). The SuperPoint-SuperGlue algorithm has been widely applied in the field of image matching in recent years due to its characteristics such as self-supervision

easy training

and high accuracy. However

in the field of heterogeneous aerospace image matching

the feature extraction ability of SuperPoint still needs to be improved. In order to improve the matching accuracy of heterogeneous aerospace images

this paper proposes a heterogeneous aerospace image matching algorithm based on the improved SuperPoint. Firstly

the spatial group-wise enhance (SGE) module and the global attention mechanism (GAM) are introduced into the SuperPoint encoder to form a supplementary encoder

which to a certain extent solves the problems of uneven distribution of image features and the difficulty in extracting features from weakly textured images. Secondly

to further enhance the feature extraction ability of the algorithm

the supplementary encoder is connected in parallel with the original SuperPoint encoder to form a combined encoder. By combining the advantages of the two

it can extract image features with greater differences

reduce the false matching of feature points in similar regions

and improve the matching accuracy of heterogeneous aerospace images. Finally

through experimental verification

within the error range of 80 pixels on the UAV-VisLoc dataset

the number of matchable images can reach 82.14%. Compared with the original SuperPoint algorithm

the number of matchable images within the error range of 80 pixels has increased by 6.05%. Compared with other advanced algorithms

the number of matchable images within each pixel error range has increased. The experiments show that the algorithm proposed in this paper can effectively solve the problems such as weak feature extraction ability and uneven feature distribution in the matching of heterogeneous aerospace images.

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references

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