2009, English, Article edition: From statistical detection to decision fusion: detection of underwater mines in high resolution SAS images Maussang, Frédéric; Chanussot, Jocelyn; Rombaut, Michèle; ...

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Bookmark: http://trove.nla.gov.au/version/209073
Physical Description
  • text
Published
  • HAL - CCSD
  • 2009
Language
  • English

Edition details

Title
  • From statistical detection to decision fusion: detection of underwater mines in high resolution SAS images
Author
  • Maussang, Frédéric
  • Chanussot, Jocelyn
  • Rombaut, Michèle
  • Amate, Maud
Published
  • HAL - CCSD
  • 2009
Physical Description
  • text
Subjects
Notes
  • Many approaches have been proposed in underwater mine detection and classification using sonar images. The goal is to evaluate a confidence that a pixel belongs to a sought object or to the seabed. In the following, considering the object characteristics (size, reflectivity), we will always assume that the detected objects are actual mines. We propose a detection method structured as a data fusion system. This type of architecture is a smart and adaptive structure: the addition or removal of parameters is easily taken into account, without any modification of the global structure. The inputs of the proposed system are the parameters extracted from an SAS image (statistical in our case). The outputs of the system are the areas detected as potentially including an object.
  • HAL:hal-00367598, version 1
  • HAL:http:/​/​hal.archives-ouvertes.fr/​hal-00367598/​en/​
Language
  • English
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