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Lunar Image Matching Based on FAST Features with Adaptive Threshold

  • You ZhaiEmail author
  • Shuai Liu
  • Xiwei Guo
  • Peng He
  • Zhuanghe Zhang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

The contrast of lunar images is low, and few features can be extracted. Therefore, lunar images can be hardly matched with high accuracy. A lunar image matching method based on features from accelerated segment test (FAST) feature and speeded-up robust features (SURFs) descriptor is presented. First, entropy of image is adopted to automatically compute threshold for extracting FAST features. Second, SURF descriptors are used to describe candidate features, and then initial matches with nearest neighborhood strategy are obtained. Third, outliers are rejected from initial matches by RANSAC-based model estimation strategy and homography constraint. Experimental results show that the proposed method can get enough image correspondences and the matching errors are less than 0.2 pixels. It indicates that the proposed method can automatically achieve high-accuracy lunar image matching and lay good foundation for subsequent lunar image stitching and fusion.

Keywords

Image matching Moon image FAST SURF 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • You Zhai
    • 1
    Email author
  • Shuai Liu
    • 2
  • Xiwei Guo
    • 1
  • Peng He
    • 1
  • Zhuanghe Zhang
    • 1
  1. 1.Shijiazhuang CampusArmy Engineering UniversityShijiazhuangChina
  2. 2.Technical Division66132 TroopsBeijingChina

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