Acta Geodaetica et Cartographica Sinica ›› 2021, Vol. 50 ›› Issue (10): 1390-1403.doi: 10.11947/j.AGCS.2021.20200587

• Photogrammetry and Remote Sensing • Previous Articles     Next Articles

A fast matching method of SAR and optical images using angular weighted orientated gradients

FAN Zhongli1, ZHANG Li1, WANG Qingdong1, LIU Siting1,2, YE Yuanxin3   

  1. 1. Chinese Academy of Surveying and Mapping, Beijing 100039, China;
    2. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China;
    3. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
  • Received:2020-12-08 Revised:2021-07-21 Published:2021-11-09
  • Supported by:
    The National Key Research and Development Program of China (No. 2019YFB1405600)

Abstract: To solve the problem of matching difficulty caused by the significant nonlinear grayscale differences between SAR and optical images, this paper proposes a fast matching algorithm based on image structural properties named SOFM(SAR-to-optical fast matching algorithm).The traditional methods based on image grayscale are generally difficult to resist the nonlinear grayscale differences between SAR and optical images, but the geometric constructs and shape features can exist stably among different types of images, so in our the proposed method both the magnitude and orientation information of image gradient are used to build a geometric structural feature descriptor named AWOG(angular weighted orientated gradients), then based on the template matching strategy, the sum of squared difference of the descriptors is used to define the similarity metric for matching and then the image matching function expressed in the frequency domain is given. A complete set of image matching process is established based on SOFM, and has been validated using multiple pairs of SAR and optical images, the results show that the proposed method can effectively resist the nonlinear grayscale differences between SAR and optical images, and outperforms the traditional classical image grayscale-based methods and existing image structural-based methods in matching performance and precision.

Key words: SAR images, optical images, structural properties, image gradient, template matching

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