Abstract
In recent years, researchers have started to apply neural networks to ship detection of synthetic aperture radar (SAR). However, SAR images are difficult to acquire and interpret manually. Sufficient training samples cannot be obtained, which limits the performance of ship detection. Therefore, data enhancement has become an active means to handle the issue of insufficient samples. To evaluate the performance of diverse data enhancement methods, a variety of data enhancement methods is used to expand the ship samples in the SAR ship detection dataset, including rotation, shift, mirror, brightening, etc. Moreover, we used the combination of diverse data enhancement methods for experiments. Experiments were performed using the SSDD dataset. Based on the experimental results, we analyze the characteristics of diverse data enhancement methods.
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