  Ship detection from satellite imagery using Deep Learning (DL) is an
indispensable solution for maritime surveillance. However, applying DL models
trained on one dataset to others having differences in spatial resolution and
radiometric features requires many adjustments. To overcome this issue, this
paper focused on the DL models trained on datasets that consist of different
optical images and a combination of radar and optical data. When dealing with a
limited number of training images, the performance of DL models via this
approach was satisfactory. They could improve 5-20% of average precision,
depending on the optical images tested. Likewise, DL models trained on the
combined optical and radar dataset could be applied to both optical and radar
images. Our experiments showed that the models trained on an optical dataset
could be used for radar images, while those trained on a radar dataset offered
very poor scores when applied to optical images.
