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New Saliency Point Detection and Evaluation Methods for Finding Structural Differences in Remote Sensing Images of Long Time-Span Samples

  • Conference paper
Advanced Concepts for Intelligent Vision Systems (ACIVS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6475))

Abstract

The paper introduces a novel methodology to find changes in remote sensing image series. Some remotely sensed areas are scanned frequently to spot relevant changes, and several repositories contain multi-temporal image samples for the same area. The proposed method finds changes in images scanned by a long time-interval difference in very different lighting and surface conditions. The presented method is basically an exploitation of Harris saliency function and its derivatives for finding featuring points among image samples. To fit together the definition of keypoints and their active contour around them, we have introduced the Harris corner detection as an outline detector instead of the simple edge functions. We also demonstrate a new local descriptor by generating local active contours. Saliency points support the boundary hull definition of objects, constructing by graph based connectivity detection and neighborhood description. This graph based shape descriptor works on the saliency points of the difference and in-layer features. We prove the method in finding structural changes on remote sensing images.

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Author information

Authors and Affiliations

  1. Pazmany Peter Catholic University, Prater 50/A, 1083, Budapest, Hungary

    Andrea Kovacs

  2. Hungarian Academy of Sciences, Computer and Automation Research Institute, Distributed Events Analysis Research Group, Kende 13-17, 1111, Budapest, Hungary

    Tamas Sziranyi

Authors
  1. Andrea Kovacs
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  2. Tamas Sziranyi
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Editor information

Editors and Affiliations

  1. DGA/D4S/MRIS, CEP/GIP, 16 bis avenue Prieur de la côte d’or, 94114, Arcueil, France

    Jacques Blanc-Talon

  2. Canon Information Systems Research Australia, Sydney, Australia

    Don Bone

  3. Telecommunications and Information processing (TELIN), Ghent University, St.-Pietersnieuwstraat 41, B9000, Ghent, Belgium

    Wilfried Philips

  4. CSIRO ICT Centre, ICT Centre, Epping, NSW 1710, Po Box 76, 1710, Sydney, Australia

    Dan Popescu

  5. Physics, University of Antwerp, Universiteitsplein 1; Building N., 2610, Wilrijk, Belgium

    Paul Scheunders

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© 2010 Springer-Verlag Berlin Heidelberg

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Kovacs, A., Sziranyi, T. (2010). New Saliency Point Detection and Evaluation Methods for Finding Structural Differences in Remote Sensing Images of Long Time-Span Samples. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17691-3_25

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  • DOI: https://doi.org/10.1007/978-3-642-17691-3_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17690-6

  • Online ISBN: 978-3-642-17691-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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