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Improvement of EPIC/DSCOVR Image Registration by Means of Automatic Coastline Detection

Remote Sensing Technology Institute, German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(15), 1747;
Received: 27 May 2019 / Revised: 19 July 2019 / Accepted: 23 July 2019 / Published: 25 July 2019
(This article belongs to the Special Issue Pattern Recognition and Image Processing for Remote Sensing)
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In this work, we address the image geolocation issue that is present in the imagery of EPIC/DSCOVR (Earth Polychromatic Imaging Camera/Deep Space Climate Observatory) Level 1B version 2. To solve it, we develop an algorithm that automatically computes a registration correction consisting of a motion (translation plus rotation) and a radial distortion. The correction parameters are retrieved for every image by means of a regularised non-linear optimisation process, in which the spatial distances between the theoretical and actual locations of chosen features are minimised. The actual features are found along the coastlines automatically by using computer vision techniques. The retrieved correction parameters show a behaviour that is related to the period of DSCOVR orbiting around the Lagrangian point L 1 . With this procedure, the EPIC coastlines are collocated with an accuracy of about 1.5 pixels, thus significantly improving the original registration of about 5 pixels from the imagery of EPIC/DSCOVR Level 1B version 2. View Full-Text
Keywords: EPIC; registration; geolocation; computer vision; regularisation EPIC; registration; geolocation; computer vision; regularisation

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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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Molina García, V.; Sasi, S.; Efremenko, D.S.; Loyola, D. Improvement of EPIC/DSCOVR Image Registration by Means of Automatic Coastline Detection. Remote Sens. 2019, 11, 1747.

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