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Constraint Model for the Satellite Image Mosaic Selection Problem (Short Paper)

Authors Manuel Combarro Simón , Pierre Talbot , Grégoire Danoy , Jedrzej Musial , Mohammed Alswaitti , Pascal Bouvry

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Author Details

Manuel Combarro Simón
  • University of Luxembourg, Luxembourg
  • Interdisciplinary Centre for Security, Reliability and Trust (SnT), Luxembourg
Pierre Talbot
  • University of Luxembourg, Luxembourg
  • Interdisciplinary Centre for Security, Reliability and Trust (SnT), Luxembourg
Grégoire Danoy
  • University of Luxembourg, Luxembourg
  • Interdisciplinary Centre for Security, Reliability and Trust (SnT), Luxembourg
Jedrzej Musial
  • Poznan University of Technology, Poland
Mohammed Alswaitti
  • University of Luxembourg, Luxembourg
  • Interdisciplinary Centre for Security, Reliability and Trust (SnT), Luxembourg
Pascal Bouvry
  • University of Luxembourg, Luxembourg
  • Interdisciplinary Centre for Security, Reliability and Trust (SnT), Luxembourg

Funding

  • Combarro Simón, Manuel: This work is partially funded by the Luxembourg National Research Fund (FNR) - ASTRAL Project, ref. 17043604, and by the joint research programme UL/SnT-ILNAS on Technical Standardization for Trustworthy ICT, Aerospace, and Construction.
  • Talbot, Pierre: This work is supported by the FNR - COMOC Project, ref. C21/IS/16101289.
  • Musial, Jedrzej: This work is funded by the FNR - PolLux program under the SERENITY Project, ref. C22/IS/17395419.

Cite AsGet BibTex

Manuel Combarro Simón, Pierre Talbot, Grégoire Danoy, Jedrzej Musial, Mohammed Alswaitti, and Pascal Bouvry. Constraint Model for the Satellite Image Mosaic Selection Problem (Short Paper). In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 44:1-44:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.CP.2023.44

Abstract

Satellite imagery solutions are widely used to study and monitor different regions of the Earth. However, a single satellite image can cover only a limited area. In cases where a larger area of interest is studied, several images must be stitched together to create a single larger image, called a mosaic, that can cover the area. Today, with the increasing number of satellite images available for commercial use, selecting the images to build the mosaic is challenging, especially when the user wants to optimize one or more parameters, such as the total cost and the cloud coverage percentage in the mosaic. More precisely, for this problem the input is an area of interest, several satellite images intersecting the area, a list of requirements relative to the image and the mosaic, such as cloud coverage percentage, image resolution, and a list of objectives to optimize. We contribute to the constraint and mixed integer lineal programming formulation of this new problem, which we call the satellite image mosaic selection problem, which is a multi-objective extension of the polygon cover problem. We propose a dataset of realistic and challenging instances, where the images were captured by the satellite constellations SPOT, Pléiades and Pléiades Neo. We evaluate and compare the two proposed models and show their efficiency for large instances, up to 200 images.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Discrete space search
Keywords
  • constraint modeling
  • satellite imaging
  • set covering
  • polygon covering

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Supplementary Materials

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