Capability of geometric features to classify ships in SAR imagery

Ship classification in synthetic aperture radar (SAR) imagery has become a new hotspot in remote sensing community for its valuable potential in many maritime applications. Several kinds of ship features, such as geometric features, polarimetric features, and scattering features have been widely applied on ship classification tasks. Compared with polarimetric features and scattering features, which are subject to SAR parameters (e.g., sensor type, incidence angle, polarization, etc.) and environment factors (e.g., sea state, wind, wave, current, etc.), geometric features are relatively independent of SAR and environment factors, and easy to be extracted stably from SAR imagery. In this paper, the capability of geometric features to classify ships in SAR imagery with various resolution has been investigated. Firstly, the relationship between the geometric feature extraction accuracy and the SAR imagery resolution is analyzed. It shows that the minimum bounding rectangle (MBR) of ship can be extracted exactly in terms of absolute precision by the proposed automatic ship-sea segmentation method. Next, six simple but effective geometric features are extracted to build a ship representation for the subsequent classification task. These six geometric features are composed of length (f1), width (f2), area (f3), perimeter (f4), elongatedness (f5) and compactness (f6). Among them, two basic features, length (f1) and width (f2), are directly extracted based on the MBR of ship, the other four are derived from those two basic features. The capability of the utilized geometric features to classify ships are validated on two data set with different image resolutions. The results show that the performance of ship classification solely by geometric features is close to that obtained by the state-of-the-art methods, which obtained by a combination of multiple kinds of features, including scattering features and geometric features after a complex feature selection process.

[1]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[2]  Ji Ke-feng,et al.  Ship recognition in high resolution SAR imagery based on feature selection , 2012, 2012 International Conference on Computer Vision in Remote Sensing.

[3]  Gerard Margarit,et al.  Ship Classification in Single-Pol SAR Images Based on Fuzzy Logic , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Carlos López-Martínez,et al.  Exploitation of Ship Scattering in Polarimetric SAR for an Improved Classification Under High Clutter Conditions , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[5]  J. L. Hodges,et al.  Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties , 1989 .

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  Jordi J. Mallorquí,et al.  Single-Pass Polarimetric SAR Interferometry for Vessel Classification , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Xi Zhang,et al.  Ship Classification in SAR Image by Joint Feature and Classifier Selection , 2016, IEEE Geoscience and Remote Sensing Letters.

[9]  Huanxin Zou,et al.  Ship Surveillance by Integration of Space-borne SAR and AIS – Review of Current Research , 2014 .

[10]  Bo Zhang,et al.  A Novel Hierarchical Ship Classifier for COSMO-SkyMed SAR Data , 2014, IEEE Geoscience and Remote Sensing Letters.

[11]  Thomas Fritz,et al.  Ship Surveillance With TerraSAR-X , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Di Zhao,et al.  Hierarchical ship detection and recognition with high-resolution polarimetric synthetic aperture radar imagery , 2014 .

[13]  Huanxin Zou,et al.  Superstructure scattering distribution based ship recognition in TerraSAR-X imagery , 2014 .

[14]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[15]  Hong Zhang,et al.  Merchant Vessel Classification Based on Scattering Component Analysis for COSMO-SkyMed SAR Images , 2013, IEEE Geoscience and Remote Sensing Letters.

[16]  D. Massart,et al.  The Mahalanobis distance , 2000 .

[17]  Guy Seguin,et al.  Evolution of the RADARSAT Program , 2014, IEEE Geoscience and Remote Sensing Magazine.

[18]  Huanxin Zou,et al.  Ship Classification in TerraSAR-X Images With Feature Space Based Sparse Representation , 2013, IEEE Geoscience and Remote Sensing Letters.

[19]  I. Hajnsek,et al.  A tutorial on synthetic aperture radar , 2013, IEEE Geoscience and Remote Sensing Magazine.

[20]  Rolf Werninghaus,et al.  The TerraSAR-X Mission and System Design , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Francesco Caltagirone,et al.  The COSMO-SkyMed Dual Use Earth Observation Program: Development, Qualification, and Results of the Commissioning of the Overall Constellation , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[22]  Jordi J. Mallorquí,et al.  A Comparative Study of Operational Vessel Detectors for Maritime Surveillance Using Satellite-Borne Synthetic Aperture Radar , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[23]  D. Hand,et al.  Idiot's Bayes—Not So Stupid After All? , 2001 .