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Learning Graph Model for Different Dimensions Image Matching

  • Haoyi ZhouEmail author
  • Xiao Bai
  • Jun Zhou
  • Haichuan Yang
  • Yun Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9069)

Abstract

Hyperspectral imagery has been widely used in real applications such as remote sensing, agriculture, surveillance, and geological analysis. Matching hyperspectral images is a challenge task due to the high dimensional nature of the data. The matching task becomes more difficult when images with different dimensions, such as a hyperspectral image and an RGB image, have to be matched. In this paper, we address this problem by investigating structured support vector machine to learn graph model for each type of image. The graph model incorporates both low-level features and stable correspondences within images. The inherent characteristics are depicted by using graph matching algorithm on weighted graph models. We validate the effectiveness of our method through experiments on matching hyperspectral images to RGB images, and hyperspectral images with different dimensions.

Keywords

Matching Graph Model Hyperspectral Image 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Haoyi Zhou
    • 1
    Email author
  • Xiao Bai
    • 1
  • Jun Zhou
    • 2
  • Haichuan Yang
    • 1
  • Yun Liu
    • 1
  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
  2. 2.School of Information and Communication TechnologyGriffith UniversityBrisbaneAustralia

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