Abstract: With the development of high resolution synthetic aperture radar (SAR) technology, ship type recognition become smore and more important in remote sensing. In order to improve the identification accuracy, a high-resolution SAR ship sample set, named as HR4S, is constructed using RADARSAT-2 and Chinese GaoFen-3 (GF-3) SAR data. The process of ship samples extraction and HR4S construction are introduced in detail. The HR4S covers 1 962 samples with different polarization modes, resolutions and ship types. The ship geometry parameters and the ship classification performance of HR4S with different classifier and features are analyzed. The results indicate that the geometrical parameters extracted fromRADARSAT-2in HH, VH and VV polarization are all better than that of GF-3. Furthermore, the direction has little influence on the geometric parameter of ships in VV polarization. In terms of ship type rec-ognition performance, the accuracy of random forest (RF) classifier achieved 61.85% on GF-3 data and 60.80% on RADARSAT-2 data. In general, the classification accuracy of GF-3 ships is better than RADARSAT-2. The HR4S constructed in this paper not only further improves the high-resolution ship samples, but also has important significance in the recognition of ship types at sea.