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|Title:||TEXTURE-BASED VESSEL CLASSIFIER FOR ELECTRO-OPTICAL SATELLITE IMAGERY|
|Authors:||FERNANDEZ ARGUEDAS VIRGINIA|
|Type:||Articles in periodicals and books|
|Abstract:||Satellite imagery provides a valuable source of information for maritime surveillance. The vast majority of the research regarding satellite imagery for maritime surveillance focuses on vessel detection and image enhancement, whilst vessel classification remains a largely unexplored research topic. This paper presents a vessel classifier for spaceborne electrooptical imagery based on a feature representative across all satellite imagery, texture. Local Binary Patterns were selected to represent vessels for their high distinctivity and low computational complexity. Considering vessels characteristic super-structure, the extracted vessel signatures are sub-divided in three sections bow, middle and stern. A hierarchical decision-level classification is proposed, analysing first each vessel section individually and then combining the results in the second stage. The proposed approach is evaluated with the electro-optical satellite image dataset presented in . Experimental results reveal an accuracy of 85.64% across four vessel categories.|
|JRC Directorate:||Space, Security and Migration|
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