Automated Photogrammetric Image Matching with Sift Algorithm and Delaunay Triangulation

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An algorithm for image matching of multi-sensor and multi-temporal satellite images is developed. The method is based on the SIFT feature detector proposed by Lowe in (Lowe, 1999). First, SIFT feature points are detected independently in two images (reference and sensed image). The features detected are invariant to image rotations, translations, scaling and also to changes in illumination, brightness and 3-dimensional viewpoint. Afterwards, each feature of the reference image is matched with one in the sensed image if, and only if, the distance between them multiplied by a threshold is shorter than the distances between the point and all the other points in the sensed image. Then, the matched features are used to compute the parameters of the homography that transforms the coordinate system of the sensed image to the coordinate system of the reference image. The Delaunay triangulations of each feature set for each image are computed. The isomorphism of the Delaunay triangulations is determined to guarantee the quality of the image matching. The algorithm is implemented in Matlab and tested on World-View 2, SPOT6 and TerraSAR-X image patches.
Original languageEnglish
JournalI S P R S Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Pages (from-to)23-28
Publication statusPublished - 2016
Event23rd Congress of the International Society of Photogrammetry and Remote Sensing: From Human History to the Future with Spatial Information - Prague, Czech Republic
Duration: 12 Jul 201619 Jul 2016
Conference number: 23


Conference23rd Congress of the International Society of Photogrammetry and Remote Sensing
CountryCzech Republic
Internet address
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Automated image matching, SIFT algorithm, Delaunay triangulation, Graph isomorphism, Multi-sensor image matching, Multi-temporal image matching

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