A Full-Level Context Squeeze-and-Excitation ROI Extractor for SAR Ship Instance Segmentation

Existing deep learning (DL)-based synthetic aperture radar (SAR) ship instance segmentation models mostly extract feature subsets at the single level of feature pyramid network (FPN), and also ignore context information of the region of interest (ROI), which both hinder accuracy improvements. Thus, a full-level context squeeze-and-excitation ROI extractor (FL-CSE-ROIE) is proposed to handle these problems. FL-CSE-ROIE has three novelties: 1) full-level, i.e., extract feature subsets at each level of FPN to retain multi-scale features; 2) context, i.e., add multi-context surroundings of different scopes to ROIs to ease background interferences; and 3) squeeze-and-excitation (SE), i.e., balance contributions of different scope contexts to highlight valuable features and suppress useless ones. FL-CSE-ROIE is applied to the fashionable hybrid task cascade (HTC) model. Results on two open SAR ship detection dataset (SSDD) and high-resolution SAR images dataset (HRSID) confirm its effectiveness. Moreover, another two improvements to HTC are also proposed to enhance accuracy further: 1) the raw deconv is replaced with a content-aware reassembly of features block (CARAFEB) to enable larger receptive fields and 2) the raw
$1\times1$
conv for the mask information interaction is replaced with a global feature self-attention block (GFSAB) to enhance interaction benefits. Finally, FL-CSE-ROIE surpasses the other nine advanced models, better than the suboptimal model by 2.4%/2.3% detection average precision (AP) and 3.0%/2.5% segmentation AP on SSDD/HRSID.