International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 77–82, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-77-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 77–82, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-77-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Feb 2020

07 Feb 2020

RESEARCH PROGRESS ON FEATURE MATCHING OF SAR AND OPTICAL IMAGES

H. Qian1, J. W. Yue1, and M. Chen2 H. Qian et al.
  • 1Faculty of Geographical Science, Beijing Normal University, No.19, XinJieKouWai St., HaiDian District, Beijing 100875, P. R. China
  • 2Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, P. R. China

Keywords: Research Progress, SAR and Optical Image, Feature Matching, Typical Method Comparison

Abstract. Before obtaining information and identifying ground target from images, image matching is necessary. However, problems of strong pixel noise interference and nonlinear gray scale differences in synthetic aperture radar image still exist. Feature matching becomes a kind possible solution. To learn the research progress of SAR and optical image matching, as well as finding solutions for above matching problems, a summary for feature matching with SAR and optical image is indispensable. By listing three typical methods below, we can discuss and compare how researchers improve and innovate methods for feature matching from different angles in matching process. First method is feature matching method proposed by CHEN Min et. It uses phase congruency method to detect point features. Feature descriptors are based on gaussian-gamma-shaped edge strength maps instead of original images. This method combines both edge features and point features to reach a match target. The second one is SAR-SIFT algorithm of F. Dellinger et. This kind of method is based on improvement of sift algorithm. It proposes a SAR-Harris method and also a calculation method for features descriptors named gradient by ratio. Thirdly, it is feature matching method proposed by Yu Qiuze et. By using edge features of image and improvement of hausdorff distance for similarity measure, it applies genetic algorithm to accelerate matching search process to complete matching tasks. Those methods are implemented by using python programs, and are compared by some indexes. Experimental data used multiple sets of terrasar and optical image pairs of different resolutions. To some extent, the results demonstrate that all three kinds of feature methods can improve the matching effect between SAR and optical images. It can be easier to reach match purposes of SAR and optical images by using image edge features, while such methods are too dependent on the edge features.