合成孔径雷达图像中小船实例分割的尺度感知维度关注网络

IF 1.4 4区 地球科学 Q4 ENVIRONMENTAL SCIENCES
Xiao Ke, Tianwen Zhang, Zikang Shao
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引用次数: 0

摘要

从合成孔径雷达(SAR)图像中分割小型船舶实例是一项具有挑战性的任务。因为小型船舶尺度较小,轮廓模糊,特征响应弱。此外,背景干扰和杂波给小型船舶的特征提取增加了难度。为了解决这一问题,我们提出了一种尺度感知维度关注网络(SA-DWA-Net),用于更好地分割SAR图像中的小型船舶实例。SA-DWA-Net采用两个子网来保证对小型船舶的实例分割。第一种是尺度感知子网络,可以充分利用底层位置敏感信息实现具有代表性的小型船舶特征。二是多维关注子网络,充分利用高级语义敏感信息进行精细的小船特征表达。我们在两个开放的SSDD和HRSID数据集上进行了实验,以验证所提出方法的有效性。定量实验结果表明,所提出的SA-DWA-Net具有最先进的SAR舰船实例分割性能。具体而言,SA-DWA-Net在SSDD上比现有最佳模型高出2.2%的盒检测平均精度(AP)和5.0%的掩码分割AP,在HRSID上比现有最佳模型高出2.9%的盒检测平均精度和3.7%的掩码分割AP。特别是,本文提出的SA-DWA-Net的小船掩模分割AP在SSDD和HRSID上分别比现有最佳模型高4.4%和3.7%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scale-aware dimension-wise attention network for small ship instance segmentation in synthetic aperture radar images
Small ship instance segmentation from synthetic aperture radar (SAR) images is a challenging task. Because small ships have smaller scales, indistinct contours, and weak feature response. In addition, background interference and clutter make feature extraction of small ships more difficult. To solve this issue, we propose a scale-aware dimension-wise attention network (SA-DWA-Net) for better small ship instance segmentation in SAR images. SA-DWA-Net has two subnetworks to ensure its desirable instance segmentation of small ships. The first is a scale-aware subnetwork that can fully use low-level location-sensitive information to achieve representative small ship features. The second is a dimension-wise attention subnetwork that can fully utilize high-level semantics-sensitive information for refined small ship feature expression. We perform experiments on two open SSDD and HRSID datasets to verify the effectiveness of the proposed method. Quantitative experimental results show the state-of-the-art SAR ship instance segmentation performance of the proposed SA-DWA-Net. Specifically, SA-DWA-Net surpasses the existing best model by 2.2% box detection average precision (AP) and 5.0% mask segmentation AP on SSDD and by 2.9% box detection AP and 3.7% mask segmentation AP on HRSID. Especially, the small ship mask segmentation AP of the proposed SA-DWA-Net is higher than the existing best model by 4.4% on SSDD and 3.7% on HRSID.
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来源期刊
Journal of Applied Remote Sensing
Journal of Applied Remote Sensing 环境科学-成像科学与照相技术
CiteScore
3.40
自引率
11.80%
发文量
194
审稿时长
3 months
期刊介绍: The Journal of Applied Remote Sensing is a peer-reviewed journal that optimizes the communication of concepts, information, and progress among the remote sensing community.
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