超尺度自导注意力网络的遥感船舶识别

传统多尺度卷积神经网络因接收域有限，难以对超尺度变化的空间目标进行建模。提出一种遥感船舶的超尺度自导注意力网络（HSSGAN）识别框架，通过组连接的轻量级超尺度子空间模块捕获船舶的超尺度特征和尺度不变性，使用自导注意力网络逐步细化超尺度特征图，并在超尺度局部和全局语义之间建立长期依赖关系以增强类之间特征图的差异性。同时，通过忽略不相关信息及聚合相关特征以增强目标船舶的识别性。实验结果表明，与TP-FCN、CF-SDN和HSF-Net方法相比，HSSGAN方法具有更好的识别效果，F1-Score值为0.966 78。

Remote Sensing Ship Recognition Based on Hyper-Scale Self-Guided Attention Networks

Due to the limited reception domain, the existing multi-scale Convolutional Neural Networks(CNN) often fail to model space targets with super-scale variation.In order to solve this problem, a Hyper-Scale Self-Guided Attention Networks(HSSGAN) recognition framework for remote sensing ships is proposed.The framework employs a lightweight super-scale subspace module connected by groups to capture the super-scale feature and scale invariance of the ship.Then the super-scale feature map is refined gradually by using the self-guided attention, and a long-term dependency relationship is established between the super-scale local and global semantics adaptively to enhance the difference of the feature maps between classes.In addition, irrelevant information is ignored while relevant features are aggregated, so the identifiability of the target ship can be enhanced.The experimental results show that the HSSGAN method exhibits improved recognition performance with the F1 value reaching 0.966 78.