LFG-Net: Low-Level Feature Guided Network for Precise Ship Instance Segmentation in SAR Images

Ship instance segmentation of high-resolution (HR) synthetic aperture radar (SAR) images is a valuable and challenging task due to the complex scattering and noise properties. In this article, we pioneered the construction of the low-level feature to discriminate the ships and complemented the super-resolution (SR) denoising techniques in the network modules, termed low-level feature guided network (LFG-Net), for precise ship instance segmentation in SAR images. LFG-Net consists of the low-level feature concerned pyramid (LFCP), the high-resolution feature interaction module (HR-FIM), and the compression recovery segmentation branch (CRSB). LFCP extends the vanilla feature pyramid network (FPN) with the
${P_{1}}$
layer and complements SR techniques to capture the regional and texture information at the image level for small object segmentation. HR-FIM interacts with the bounding box region of interest (RoI) feature and mask RoI feature at the instance level with HR techniques to enhance the mask RoI feature. CRSB aims at recovering the HR mask predictions to improve the ship segmentation performance. Comprehensive experiments on high-resolution SAR images dataset (HRSID), polygon segmentation SAR ship detection dataset (PSeg-SSDD), and AirSARShip indicate that LFG-Net* achieves 11.7%, 6.3%, and 12.7% AP increments compared with the Mask R-CNN baseline, respectively. Besides, it receives 9.5%, 4.9%, and 7.3% AP increments compared with the state-of-the-art method, which bridges the gap of instance segmentation precision in SAR images. In terms of the visualized instance segmentation results, LFG-Net
^*
is capable of segmenting the complex scenes, e.g., the adjacent distributed ships and ships with strong reflection noise interference, in SAR images. The code is available at:
https://github.com/Evarray/LFG-Net
.