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A Clustering Approach to Heterogeneous Change Detection

  • Luigi Tommaso LuppinoEmail author
  • Stian Normann Anfinsen
  • Gabriele Moser
  • Robert Jenssen
  • Filippo Maria Bianchi
  • Sebastiano Serpico
  • Gregoire Mercier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10270)

Abstract

Change detection in heterogeneous multitemporal satellite images is a challenging and still not much studied topic in remote sensing and earth observation. This paper focuses on comparison of image pairs covering the same geographical area and acquired by two different sensors, one optical radiometer and one synthetic aperture radar, at two different times. We propose a clustering-based technique to detect changes, identified as clusters that split or merge in the different images. To evaluate potentials and limitations of our method, we perform experiments on real data. Preliminary results confirm the relationship between splits and merges of clusters and the occurrence of changes. However, it becomes evident that it is necessary to incorporate prior, ancillary, or application-specific information to improve the interpretation of clustering results and to identify unambiguously the areas of change.

Keywords

Domain adaptation Heterogeneous image sources Change detection Clustering 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Machine Learning GroupUniversity of TromsøTromsøNorway
  2. 2.DITEN DepartmentUniversity of GenoaGenoaItaly
  3. 3.Dpt. Image et Traitement Information, Telecom BretagneBrestFrance

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