  Fine-grained ship instance segmentation in satellite images holds
considerable significance for monitoring maritime activities at sea. However,
existing datasets often suffer from the scarcity of fine-grained information or
pixel-wise localization annotations, as well as the insufficient image
diversity and variations, thus limiting the research of this task. To this end,
we propose a benchmark dataset for fine-grained Ship Instance Segmentation in
Panchromatic satellite images, namely SISP, which contains 56,693
well-annotated ship instances with four fine-grained categories across 10,000
sliced images, and all the images are collected from SuperView-1 satellite with
the resolution of 0.5m. Targets in the proposed SISP dataset have
characteristics that are consistent with real satellite scenes, such as high
class imbalance, various scenes, large variations in target densities and
scales, and high inter-class similarity and intra-class diversity, all of which
make the SISP dataset more suitable for real-world applications. In addition,
we introduce a Dynamic Feature Refinement-assist Instance segmentation network,
namely DFRInst, as the benchmark method for ship instance segmentation in
satellite images, which can fortify the explicit representation of crucial
features, thus improving the performance of ship instance segmentation.
Experiments and analysis are performed on the proposed SISP dataset to evaluate
the benchmark method and several state-of-the-art methods to establish
baselines for facilitating future research. The proposed dataset and source
codes will be available at: https://github.com/Justlovesmile/SISP.
