Anchor-Free_SAR_Ship_Instance_Segmentation_With_Centroid-Distance_Based_Loss.pdf (9.14 MB)
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Anchor-free SAR Ship Instance Segmentation with Centroid-distance Based Loss

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journal contribution
posted on 22.11.2021, 11:32 by F Gao, Y Huo, J Wang, A Hussain, Huiyu Zhou
Instance segmentation methods for synthetic aperture radar (SAR) ship imaging have certain unsolved problems. 1) Most of the anchor-based detection algorithms encounter difficulties in tuning the anchor-related parameters and high computational costs. 2) Different tasks share the same features without considering the differences between tasks, leading to mismatching of the shared features and inconsistent training targets. 3) Common loss functions for instance segmentation cannot effectively distinguish the positional relationships between ships with the same degree of overlap. In order to alleviate these problems, we first adopt a lightweight feature extractor and an anchor-free convolutional network, which effectively help to reduce computational consumption and model complexity. Second, to fully disseminate feature information, a dynamic encoder–decoder is proposed to dynamically transform the shared features to task-specific features in channel and spatial dimensions. Third, a novel loss function based on centroid distance is designed to make full use of the geometrical shape and positional relationship between SAR ship targets. In order to better extract features from SAR images in complex scenes, we further propose the dilated convolution enhancement module, which utilizes multiple receptive fields to take full advantage of the shallow feature information. Experiments conducted on the SAR ship detection dataset prove that the method proposed in this article is superior to the other state-of-the-art algorithms in terms of instance segmentation accuracy and model complexity.

History

Citation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 14), pp.11352 - 11371.

Author affiliation

School of Informatics

Version

VoR (Version of Record)

Published in

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volume

14

Pagination

11352 - 11371

Publisher

Institute of Electrical and Electronics Engineers

issn

1939-1404

Acceptance date

25/10/2021

Copyright date

2021

Available date

22/11/2021

Language

en