论文标题
轻巧的多尺度上下文网络，用于光学遥感图像中的显着对象检测
A lightweight multi-scale context network for salient object detection in optical remote sensing images
论文作者
论文摘要
由于光学遥感图像（RSIS）中更复杂的前景和背景更为戏剧化，光RSIS的显着对象检测（SOD）成为巨大的挑战。但是，与自然场景图像（NSIS）不同，有关光学RSI SOD任务的讨论仍然很少。在本文中，我们提出了一个多尺度上下文网络，即MSCNET，以用于光学RSIS中的SOD。具体而言，采用多尺度上下文提取模块来通过有效学习多尺度上下文信息来解决显着对象的规模变化。同时，为了准确检测复杂背景中的完整显着物体，我们设计了一种基于注意力的金字塔特征聚合机制，用于从多规模上下文提取模块逐渐聚集和完善显着区域。对两个基准测试的广泛实验表明，MSCNET仅使用326万参数实现竞争性能。该代码将在https://github.com/nuaayh/mscnet上找到。
Due to the more dramatic multi-scale variations and more complicated foregrounds and backgrounds in optical remote sensing images (RSIs), the salient object detection (SOD) for optical RSIs becomes a huge challenge. However, different from natural scene images (NSIs), the discussion on the optical RSI SOD task still remains scarce. In this paper, we propose a multi-scale context network, namely MSCNet, for SOD in optical RSIs. Specifically, a multi-scale context extraction module is adopted to address the scale variation of salient objects by effectively learning multi-scale contextual information. Meanwhile, in order to accurately detect complete salient objects in complex backgrounds, we design an attention-based pyramid feature aggregation mechanism for gradually aggregating and refining the salient regions from the multi-scale context extraction module. Extensive experiments on two benchmarks demonstrate that MSCNet achieves competitive performance with only 3.26M parameters. The code will be available at https://github.com/NuaaYH/MSCNet.
