Ship detection in optical remote sensing images plays a significant role in a wide range of civilian and military tasks. However, it is still a challenging issue owing to complex environmental interferences and a large variety of target scales and positions. To overcome these limitations, we propose a supervised multi-scale attention-guided detection framework, which can effectively detect ships of different scales both in complex pure ocean and port scenes. Specifically, a multi-scale supervision module is first proposed to adjust the semantic consistency of different feature levels, obtaining extracted features with small semantic gaps. Next, an attention-guided module is used to aggregate context information from both the spatial and channel dimensions by calculating map correlations, adaptively enhancing the feature representation. Moreover, to preserve the attribute and spatial relationship of the optimized features, we adopt a capsule-based module as the classifier and obtain satisfactory classification performance. Experimental results conducted on two public high-quality datasets demonstrate that the proposed method obtains state-of-the-art performance in comparison with several advanced methods.

中文翻译：

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光学遥感图像中的监督多尺度注意力引导船舶检测

光学遥感图像中的船舶检测在广泛的民用和军事任务中发挥着重要作用。然而，由于复杂的环境干扰和各种各样的目标尺度和位置，它仍然是一个具有挑战性的问题。为了克服这些限制，我们提出了一种有监督的多尺度注意力引导检测框架，可以在复杂的纯海洋和港口场景中有效地检测不同尺度的船舶。具体来说，首先提出了一种多尺度监督模块来调整不同特征级别的语义一致性，从而获得具有较小语义差距的提取特征。接下来，注意力引导模块用于通过计算地图相关性来聚合来自空间和通道维度的上下文信息，从而自适应地增强特征表示。而且，为了保留优化特征的属性和空间关系，我们采用基于胶囊的模块作为分类器，并获得了令人满意的分类性能。在两个公共高质量数据集上进行的实验结果表明，与几种先进的方法相比，所提出的方法获得了最先进的性能。