Ship Detection based on SAR Imaging Supervised by Noisy Ship Direction

Huang, Jiangyi; Niu, Yuzhen; Gan, Weize

doi:10.1109/prai53619.2021.9551103

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…Moreover, the above traditional methods overly rely on manual features and are time and labor consuming [15][16][17]. in the work of Huang et al [57]. Moreover, Guo et al [58] and Cui et al [59] used CenterNet to design more flexible networks.…”

Section: Introductionmentioning

confidence: 99%

Scale in Scale for SAR Ship Instance Segmentation

et al. 2023

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Ship instance segmentation in synthetic aperture radar (SAR) images can provide more detailed location information and shape information, which is of great significance for port ship scheduling and traffic management. However, there is little research work on SAR ship instance segmentation, and the general accuracy is low because the characteristics of target SAR ship task, such as multi-scale, ship aspect ratio, and noise interference, are not considered. In order to solve these problems, we propose an idea of scale in scale (SIS) for SAR ship instance segmentation. Its essence is to establish multi-scale modes in a single scale. In consideration of the characteristic of the targeted SAR ship instance segmentation task, SIS is equipped with four tentative modes in this paper, i.e., an input mode, a backbone mode, an RPN mode (region proposal network), and an ROI mode (region of interest). The input mode establishes multi-scale inputs in a single scale. The backbone mode enhances the ability to extract multi-scale features. The RPN mode makes bounding boxes better accord with ship aspect ratios. The ROI mode expands the receptive field. Combined with them, a SIS network (SISNet) is reported, dedicated to high-quality SAR ship instance segmentation on the basis of the prevailing Mask R-CNN framework. For Mask R-CNN, we also redesign (1) its feature pyramid network (FPN) for better small ship detection and (2) its detection head (DH) for a more refined box regression. We conduct extensive experiments to verify the effectiveness of SISNet on the open SSDD and HRSID datasets. The experimental results reveal that SISNet surpasses the other nine competitive models. Specifically, the segmentation average precision (AP) index is superior to the suboptimal model by 4.4% on SSDD and 2.5% on HRSID.

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