TWC-Net: A SAR Ship Detection Using Two-Way Convolution and Multiscale Feature Mapping
TWC-Net: A SAR Ship Detection Using Two-Way Convolution and Multiscale Feature Mapping
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TWC-Net：使用双向卷积和多尺度特征映射的 SAR 船舶检测
DOI：
10.3390/rs13132558
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发表时间：
2021
期刊：
影响因子：
5
通讯作者：
Hu Haicheng
中科院分区：
文献类型：
--
作者：
于蕾;Wu Haoyu;Zhong Zhi;郑丽颖;Deng Qiuyue;Hu Haicheng
合成孔径雷达（SAR）是一种主动对地观测系统，具有一定的对地突防能力，可进行全天候、全天候观测。利用SAR进行船舶探测对海上安全和港口管理具有重要意义。随着深度学习在普通图像中的广泛应用和良好效果，越来越多的检测算法开始进入遥感图像领域。SAR图像具有目标小、噪声高、目标稀疏等特点。两阶段检测方法，如快速区域卷积神经网络（faster RCNN），在基于SAR图的舰船目标检测中取得了良好的效果，但其效率较低且结构占用大量计算资源，不适合实时检测。单弹多盒检测器（SSD）等单阶段目标检测方法弥补了两阶段算法在速度上的不足，但缺乏对不同层信息的有效利用，因此在小目标检测方面不如两阶段算法。提出了基于双向卷积结构的双向卷积网络（TWC-Net），并利用多尺度特征映射对SAR图像进行处理。双向卷积模块可以有效地从SAR图像中提取特征，多尺度映射模块可以有效地处理浅层和深层特征信息。TWC-Net可以避免特征提取过程中小目标信息的丢失，同时保证深度特征映射对大目标的良好感知。我们使用通用SAR船舶数据集SSDD测试了我们提出的方法的性能。实验结果表明，该方法具有较高的查全率和查准率，F-Measure达93.32%。与其他方法相比，它具有更小的参数和更小的内存消耗，并且优于其他方法。
Synthetic aperture radar (SAR) is an active earth observation system with a certain surface penetration capability and can be employed to observations all-day and all-weather. Ship detection using SAR is of great significance to maritime safety and port management. With the wide application of in-depth learning in ordinary images and good results, an increasing number of detection algorithms began entering the field of remote sensing images. SAR image has the characteristics of small targets, high noise, and sparse targets. Two-stage detection methods, such as faster regions with convolution neural network (Faster RCNN), have good results when applied to ship target detection based on the SAR graph, but their efficiency is low and their structure requires many computing resources, so they are not suitable for real-time detection. One-stage target detection methods, such as single shot multibox detector (SSD), make up for the shortage of the two-stage algorithm in speed but lack effective use of information from different layers, so it is not as good as the two-stage algorithm in small target detection. We propose the two-way convolution network (TWC-Net) based on a two-way convolution structure and use multiscale feature mapping to process SAR images. The two-way convolution module can effectively extract the feature from SAR images, and the multiscale mapping module can effectively process shallow and deep feature information. TWC-Net can avoid the loss of small target information during the feature extraction, while guaranteeing good perception of a large target by the deep feature map. We tested the performance of our proposed method using a common SAR ship dataset SSDD. The experimental results show that our proposed method has a higher recall rate and precision, and the F-Measure is 93.32%. It has smaller parameters and memory consumption than other methods and is superior to other methods.
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影响因子：
3.9
作者：
Wei, Shunjun;Zeng, Xiangfeng;Shi, Jun
通讯作者：
Shi, Jun
DOI：
10.1109/igarss.2017.8127094
发表时间：
2017-07
期刊：
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
影响因子：
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DOI：
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发表时间：
2020-02-01
影响因子：
23.6
作者：
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通讯作者：
Dollar, Piotr
DOI：
10.1109/igarss.2018.8518589
发表时间：
2018-07
期刊：
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
影响因子：
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作者：
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影响因子：
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作者：
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