Scale in Scale for SAR Ship Instance Segmentation
نویسندگان
چکیده
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 traffic management. However, there little research work on SAR segmentation, the general accuracy low because characteristics target task, such as multi-scale, aspect ratio, noise interference, are not considered. In order to solve these problems, we propose an idea scale (SIS) segmentation. Its essence establish multi-scale modes a single scale. consideration characteristic targeted SIS equipped with four tentative this paper, i.e., input mode, backbone RPN mode (region proposal network), ROI interest). The establishes inputs enhances ability extract features. makes bounding boxes better accord ratios. expands receptive field. Combined them, network (SISNet) reported, dedicated high-quality basis prevailing Mask R-CNN framework. For R-CNN, also redesign (1) its feature pyramid (FPN) small detection (2) head (DH) refined box regression. We conduct extensive experiments verify effectiveness SISNet open SSDD HRSID datasets. experimental results reveal that surpasses other nine competitive models. Specifically, average precision (AP) index superior suboptimal model by 4.4% 2.5% HRSID.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2023
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs15030629