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Sensors 2018, 18(5), 1599; https://doi.org/10.3390/s18051599

Automated Geo/Co-Registration of Multi-Temporal Very-High-Resolution Imagery

1
School of Convergence & Fusion System Engineering, Kyungpook National University, Sangju 37224, Korea
2
Department of Civil Engineering, Korea Maritime and Ocean University, Busan 49112, Korea
*
Author to whom correspondence should be addressed.
Received: 16 April 2018 / Revised: 14 May 2018 / Accepted: 16 May 2018 / Published: 17 May 2018
(This article belongs to the Special Issue Spatial Analysis and Remote Sensing)
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Abstract

For time-series analysis using very-high-resolution (VHR) multi-temporal satellite images, both accurate georegistration to the map coordinates and subpixel-level co-registration among the images should be conducted. However, applying well-known matching methods, such as scale-invariant feature transform and speeded up robust features for VHR multi-temporal images, has limitations. First, they cannot be used for matching an optical image to heterogeneous non-optical data for georegistration. Second, they produce a local misalignment induced by differences in acquisition conditions, such as acquisition platform stability, the sensor’s off-nadir angle, and relief displacement of the considered scene. Therefore, this study addresses the problem by proposing an automated geo/co-registration framework for full-scene multi-temporal images acquired from a VHR optical satellite sensor. The proposed method comprises two primary steps: (1) a global georegistration process, followed by (2) a fine co-registration process. During the first step, two-dimensional multi-temporal satellite images are matched to three-dimensional topographic maps to assign the map coordinates. During the second step, a local analysis of registration noise pixels extracted between the multi-temporal images that have been mapped to the map coordinates is conducted to extract a large number of well-distributed corresponding points (CPs). The CPs are finally used to construct a non-rigid transformation function that enables minimization of the local misalignment existing among the images. Experiments conducted on five Kompsat-3 full scenes confirmed the effectiveness of the proposed framework, showing that the georegistration performance resulted in an approximately pixel-level accuracy for most of the scenes, and the co-registration performance further improved the results among all combinations of the georegistered Kompsat-3 image pairs by increasing the calculated cross-correlation values. View Full-Text
Keywords: georegistration; co-registration; Kompsat-3; registration noise georegistration; co-registration; Kompsat-3; registration noise
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Han, Y.; Oh, J. Automated Geo/Co-Registration of Multi-Temporal Very-High-Resolution Imagery. Sensors 2018, 18, 1599.

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