Remote sensing image registration algorithm based on entropy constrained and KAZE feature extraction

Wen-xing BAO; Si-er SANG; Xiang-fei SHEN

doi:10.3788/OPE.20202808.1810

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Remote sensing image registration algorithm based on entropy constrained and KAZE feature extraction

Information Sciences | 更新时间：2020-08-14

- Remote sensing image registration algorithm based on entropy constrained and KAZE feature extraction
- Optics and Precision Engineering Vol. 28, Issue 8, Pages: 1810-1819(2020)
- 作者机构：
  
  北方民族大学计算机科学与工程学院, 宁夏银川 750021
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20202808.1810
  CLC： TP394.1;TH691.9
- Received：06 January 2020，
  
  Revised：02 March 2020，
  
  Accepted：02 March 2020，
  
  Published：25 August 2020
- 稿件说明：
移动端阅览
Wen-xing BAO, Si-er SANG, Xiang-fei SHEN. Remote sensing image registration algorithm based on entropy constrained and KAZE feature extraction[J]. Optics and precision engineering, 2020, 28(8): 1810-1819.
DOI：

Wen-xing BAO, Si-er SANG, Xiang-fei SHEN. Remote sensing image registration algorithm based on entropy constrained and KAZE feature extraction[J]. Optics and precision engineering, 2020, 28(8): 1810-1819. DOI： 10.3788/OPE.20202808.1810.

摘要

针对KAZE算法在遥感图像配准过程中存在的检测速度慢和误匹配的问题，本文提出了一种改进的基于信息熵约束和KAZE特征提取的预处理算法。首先采用非重叠滑动窗口遍历遥感图像并分割窗口，计算分割后窗口区域的信息熵。然后，根据获取的信息熵形成的直方图，选取适当阈值来保留具有高信息熵的图像局部区域用于KAZE算法特征提取。最后，采用RANSAC算法去除误匹配以优化匹配结果。在SPOT、高分二号等卫星数据上的实验结果表明，本文算法相比于KAZE算法的特征点匹配精度分别提升了0.2%和0.3%，算法运行时间分别降低了70%和53%。

Abstract

The KAZE algorithm typically extracts feature points of low accuracy and mismatches in remote sensing images.Thus

this paper proposed a preprocessing algorithm to accelerate KAZE feature extraction. The proposed algorithm preprocessed the remote sensing image based on entropy constrained and KAZE feature extraction. The method first used a non-overlapping sliding window to traverse the remote sensing image and segmented the window area

and the entropy of the segmented window area was sequentially calculated.According to the histogram formed by the obtained entropy

an appropriate threshold was then selected to retain the local area of the image with high entropy for the KAZE algorithm feature extraction.Finally

the RANSAC algorithm was used to remove mismatches to optimize matching results. Experiments on the SPOT

GH-2 satellite data indicate that compared with the KAZE algorithm alone

the accuracy of the KAZE algorithm coupled with the proposed algorithm is improved by 0.2%

0.3%

and the performance time of the algorithm is reduced by 70%

53%

respectively.

关键词

Keywords

references

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