Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B6, 107-110, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
26 Jul 2012
W. C. Chang1 and L. C. Chen2 1Dept. of Civil Engineering, National Central University, Taiwan
2Center for Space and Remote Sensing Research, National Central University, Taiwan
Keywords: Matching, Feature, Aerial, Image, Photogrammetry Abstract. Image matching is a practical way to build up the relationship between point pairs. In remote sensing and photogrammetry applications, area-based matching algorithms are usually used under control environment, such as fixing exterior orientation parameters, to reach high matching precision. In order to improve the reliability of image matching, area-based matching algorithms are frequently performed with image pyramid and epipolar constraints. Thus, Central-Left-Right matching (CLR matching) is proposed to enhance the reliability. The method is designed to cope with unreliable matching when the objects are with surface discontinuity. The CLR matching puts a candidate point in the center, left and right windows. Thus, the elements of target window and search window may be corresponded to the same objects if the candidate point is located in a vertical edge. In addition, the directions of features in the image may be varied because of the object diversity. Thus, the CLR matching would be better to combine with multi-window operation in accordance with the feature direction. This paper analyzed the feature direction first followed by the multi-window matching. The major works in this investigation contain feature extraction, feature analysis, and image matching. The experiments compared the results of traditional image matching, CLR matching, and multi-window matching. The experimental results indicate that the proposed method improves the accuracy of image matching.
Conference paper (PDF, 3510 KB)

Citation: Chang, W. C. and Chen, L. C.: FEATURE ANALYSIS FOR MULTI-WINDOW MATCHING, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B6, 107-110,, 2012.

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