Contextual Squeeze-and-Excitation Mask R-CNN for SAR Ship Instance Segmentation

Ship detection and ship classification using synthetic aperture radar (SAR) have been extensively studied. Yet, SAR ship segmentation unexpectedly receives less attention. Therefore, we will supplement the blank of such study in this paper. Specifically, we present a novel contextual squeeze-and-excitation Mask R-CNN (C-SE Mask R-CNN) dedicated to ship instance segmentation in SAR images. Note that the instance segmentation simultaneously considers ship detection and ship segmentation. Intuitively, C-SE Mask R-CNN is a variant of Mask R-CNN from the computer vision community. It embeds a contextual squeeze-andexcitation module (C-SE Module) into RoIAlign of Mask R-CNN to capture prominent different levels of backgrounds' contextual information. Experimental results on the public PSeg-SSDD dataset reveal the objective accuracy progress (i.e. a 1.4% AP gain on the detection task meanwhile a 0.9% AP gain on the segmentation task) of C-SE Mask R-CNN, in contrast to the vanilla Mask R-CNN.