Abstract
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.
Highlights
During the past two decades, ocean ship monitoring and recognition has raised much attention in the remote sensing community, with applications in maritime management, fishing law enforcement, illegal immigration monitoring and rescue, safe shipping and oil spill detection
The most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy
A total of 111 ships were used in the classification process, while Automatic Identification Systems (AIS) data were applied to verify the effectiveness of the algorithm
Summary
During the past two decades, ocean ship monitoring and recognition has raised much attention in the remote sensing community, with applications in maritime management, fishing law enforcement, illegal immigration monitoring and rescue, safe shipping and oil spill detection. The geometric properties of the polarimetric scattering behaviour can act as a good estimate of the vessel’s category under investigation This method requires fully polarimetric data and the adoption from ship recognition systems is rather limited, mainly due to the increased cost of polarimetric data with respect to single-pol (or even dual-pol) ones. Besides research on ISAR and polarimetric data that impose the limitations addressed previously, various feature extraction and selection techniques have been proposed for ship pattern analysis and classification. Since 2007 and the successful launch of a series of highresolution SAR satellites, new possibilities to ship recognition and classification emerged Both Cosmo-SkyMed and TerraSAR-X missions were able to capture SAR images with more than 3m pixel size, making feature extraction and analysis techniques more efficient, and, thereafter, providing ship recognition systems of higher accuracy and robustness.
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