Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 495-499, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-495-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 495-499, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-495-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

AUTOMATED MOSAICKING OF GEOSTATIONARY OCEAN COLOR IMAGER BY COMBINATION OF SPATIAL AND FREQUENCY MATCHING

H. G. Kim1, J. H. Son2, and T. Kim2 H. G. Kim et al.
  • 13DLabs, Republic of Korea
  • 2Dept. of Geoinformatic Engineering, Inha University, 100 Inharo, Namgu, Incheon, Republic of Korea

Keywords: GOCI, Ocean Image, Mosaicking, Geometric Correction, Frequency Matching

Abstract. In general, image mosaicking is a useful and important processing for handling images with narrow field of view. It is being used widely for images from commercial cameras as well as from aerial and satellite cameras. For mosaicking images with geometric distortion, geometric correction of each image should be performed before combining images. However, automated mosaicking images with geometric distortion is not a trivial task. The goal of this paper is the development of automated mosaicking techniques applicable to handle GOCI images. In this paper, we try to extract tie-points by using spatial domain and frequency domain matching and perform the mosaicking of GOCI. The method includes five steps. First, we classify GOCI image slots according to the existence of shorelines by spatial domain matching. Second, we perform precise geometric correction on the slots with shorelines. Third, we perform initial sensor modelling for the slots without shorelines and apply geometric correction based on the initial model. Fourth, the relative relationship between the slots without shorelines and the slots with shorelines is estimated through frequency domain matching. Lastly, mosaicking of geometrically corrected all 16 image slots is performed. The proposed method was verified by applying to real GOCI images. The proposed method was able to perform automated mosaicking even for images without shorelines, and its accuracy and processing time were satisfactory. For future research, we will improve frequency matching to generate multiple tie-points and to analyse the applicability of precise sensor modelling directly from frequency matching. It is expected that the proposed method can be applied to the follow-up sensor of the GOCI, GOCI-II, and other ocean satellite images.