The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 975–982, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-975-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 975–982, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-975-2015

  30 Apr 2015

30 Apr 2015

VESSEL CLASSIFICATION IN COSMO-SKYMED SAR DATA USING HIERARCHICAL FEATURE SELECTION

A. Makedonas1, C. Theoharatos2, V. Tsagaris2, V. Anastasopoulos1, and S. Costicoglou3 A. Makedonas et al.
  • 1Electronics Laboratory (ELLAB), Physics Department, University of Patras, Patras 26500, Greece
  • 2Computer Vision Systems, IRIDA Labs S.A., Patras Science Park, Stadiou Str., Platani, 26504 Patras, Greece
  • 3Space Hellas S.A., 312 Messogion Ave., 15341 Athens, Greece

Keywords: Ship classification, COSMO-SkyMed data, high resolution SAR imagery, vessel recognition, feature extraction, hierarchical feature selection

Abstract. SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features’ statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.