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ISPRS Int. J. Geo-Inf. 2018, 7(6), 229; https://doi.org/10.3390/ijgi7060229

Automated Orthorectification of VHR Satellite Images by SIFT-Based RPC Refinement

1
Graduate School of Science Engineering and Technology, Istanbul Technical University, ITU Ayazaga Campus, Sariyer 34469, Istanbul, Turkey
2
Geomatics Engineering Department, Civil Engineering Faculty, Istanbul Technical University, ITU Ayazaga Campus, Sariyer 34469, Istanbul, Turkey
*
Author to whom correspondence should be addressed.
Received: 28 April 2018 / Revised: 6 June 2018 / Accepted: 18 June 2018 / Published: 20 June 2018
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Abstract

Raw remotely sensed images contain geometric distortions and cannot be used directly for map-based applications, accurate locational information extraction or geospatial data integration. A geometric correction process must be conducted to minimize the errors related to distortions and achieve the desired location accuracy before further analysis. A considerable number of images might be needed when working over large areas or in temporal domains in which manual geometric correction requires more labor and time. To overcome these problems, new algorithms have been developed to make the geometric correction process autonomous. The Scale Invariant Feature Transform (SIFT) algorithm is an image matching algorithm used in remote sensing applications that has received attention in recent years. In this study, the effects of the incidence angle, surface topography and land cover (LC) characteristics on SIFT-based automated orthorectification were investigated at three different study sites with different topographic conditions and LC characteristics using Pleiades very high resolution (VHR) images acquired at different incidence angles. The results showed that the location accuracy of the orthorectified images increased with lower incidence angle images. More importantly, the topographic characteristics had no observable impacts on the location accuracy of SIFT-based automated orthorectification, and the results showed that Ground Control Points (GCPs) are mainly concentrated in the “Forest” and “Semi Natural Area” LC classes. A multi-thread code was designed to reduce the automated processing time, and the results showed that the process performed 7 to 16 times faster using an automated approach. Analyses performed on various spectral modes of multispectral data showed that the arithmetic data derived from pan-sharpened multispectral images can be used in automated SIFT-based RPC orthorectification. View Full-Text
Keywords: VHR image; automated orthorectification; SIFT algorithm; incidence angle; topography; land cover VHR image; automated orthorectification; SIFT algorithm; incidence angle; topography; land cover
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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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Kartal, H.; Alganci, U.; Sertel, E. Automated Orthorectification of VHR Satellite Images by SIFT-Based RPC Refinement. ISPRS Int. J. Geo-Inf. 2018, 7, 229.

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