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A coarse-to-fine image registration method based on visual attention model

一种基于视觉显著模型的快速图像配准方法

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Abstract

Image registration is fundamental and crucial to remote sensing. However getting highly accurate registration performance automatically and fast for large-field images consistently is a challenge. As a work around to this problem, we propose a new image registration concept based on visual attention in this paper. This concept employs the advantages of feature-based or area-based methods to improve the precision and efficiency of image registration. The key concept of proposed integrated scheme is to make optimum use of the highly prominent details in the full scene by means of visual attention computational mechanism. To testify the validation, comparisons with other classical methods are carried out on real-world images. The experimental results show that the proposed method can effectively perform on multi-view/multi-temporal remote sensing images with outstanding precision and time saving performance.

概要

创新点

本文主要从提高遥感图像配准算法效率与准确率角度出发, 受到视觉注意计算模型启发, 提出了一种由粗到精的大视场遥感图像配准策略. 本策略包括基于显著感兴趣区域的粗匹配和局部互相关的精匹配两个过程. 创新点主要包括三个方面: 1) 改进的显著性区域特征提取及干扰区域剔除策略可有效、 快速地确定同名匹配特征区域; 2) 由匹配的同名区域, 为后续局部互相关匹配提出了一种初始变换参数提取策略; 3) 互相关优化搜索方法在经过粗匹配筛选的显著感兴趣区域进行, 有效地降低了迭代搜索过程的计算量, 提高算法效率. 实验结果表明, 本文所述方法在多视角、 多时段大视场同源遥感图像配准领域有较好的应用效果.

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Authors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

    Jing Feng, Long Ma & He Chen

  2. College of Information Engineering, North China University of Technology, Beijing, 100144, China

    FuKun Bi

  3. College of Information Technology, Beijing Union University, Beijing, 100101, China

    XueJing Zhang

Authors
  1. Jing Feng
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  2. Long Ma
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  3. FuKun Bi
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  4. XueJing Zhang
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  5. He Chen
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Correspondence to FuKun Bi.

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Feng, J., Ma, L., Bi, F. et al. A coarse-to-fine image registration method based on visual attention model. Sci. China Inf. Sci. 57, 1–10 (2014). https://doi.org/10.1007/s11432-014-5207-4

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  • DOI: https://doi.org/10.1007/s11432-014-5207-4

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