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娄欣，王晗，卢昊，张文驰.2024.生成式知识迁移的SAR舰船检测.遥感学报，28（2）： 470-480
Lou X，Wang H，Lu H and Zhang W C. 2024. SAR ship detection through generative knowledge transfer. National Remote Sensing Bulletin， 28（2）：470-480
为解决基于深度卷积神经网络进行SAR舰船检测网络训练过程中数据获取、数据标注等问题，本文提出一种生成式知识迁移的SAR舰船检测框架，该框架由生成式知识迁移网络和舰船检测网络两部分组成。通过知识迁移网络生成与有标注的光学遥感图像空间分布一致且包含SAR图像特征的带标注模拟图像；使用所生成的带标注模拟图像，进一步优化舰船检测网络，以提高基于深度卷积神经网络的舰船检测的泛化性能。SAR-Ship-Detection-Datasets（SSDD）和AIR-SARShip-1.0两个公开数据集上的实验结果表明，该框架有效提高了在仅包含少量标注SAR图像样本情况下的舰船目标检测效果，可显著降低舰船在复杂背景图像中漏检和误检的概率。
To address data acquisition and labeling data in the training process of SAR ship-detection network based on deep convolutional neural network
we propose a SAR ship-detection framework via generative knowledge transfer of a knowledge transfer network for SAR image generation and a SAR ship-detection network. The knowledge transfer network consists of three parts: a cycle consistency GAN to synthesize virtual features which have spatial distribution of optical image domain and feature distribution of SAR image domain as well; We further use an identity loss to encourage pseudo-SAR images generated by the knowledge transfer networks to have more of the intrinsic features of SAR images. To alleviate the SAR feature confusion issue
we introduce a feature boundary decision loss to maximize the decision boundary of real SAR features and the pseudo ones. Therefore The knowledge transfer network generates pseudo-SAR images consistent with the spatial distribution of labeled optical remote-sensing images and has a feature distribution similar to those of SAR images. Our proposed method is evaluated from three aspects: (1) The evaluation on the generated pseudo-SAR images. When the object detection network is trained on 70% of SSDD and the pseudo-SAR images
The remaining 30% of SSDD is test set
the AP can reaches 97.50%. As for 0%
10%
20%
30%
and 50% of SSDD
the AP is 64.55%
91.14%
94.69%
96.21%
and 96.84%
respectively. When there is no real SAR images involved in the training process
Ap can still reach 64.55%. (2) Ablation study on loss functions. On the basis of using cycle consistency loss in knowledge transfer network，the best performance comes when applying both the identity loss and the feature boundary decision loss
the AP reaches 64.55%. (3) The evaluation on the ship detection network. The generated pseudo-SAR images in this paper are used in the training process of SSD
Faster R-CNN and YOLOv3 detection networks
which can increase the object detection network to learn more parameters suitable for SAR images
thus improving the detection effect of the network. Experiments in the above three aspects prove the effectiveness of the proposed method.
SAR目标检测深度学习图像生成迁移学习
SARobject detectiondeep learningimage generationgenerative adversarial networks
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