The spatial resolution of remote sensing images is continuously improved by the development of remote sensing satellite and sensor technology. Hence, background information in an image becomes increasingly complex and causes considerable interference to the object detection task. Can we pay as much attention to the object in an image as human vision does? This letter proposes a multi-scale spatial and channel-wise attention (MSCA) mechanism to answer this question. MSCA has two advantages that help improve object detection performance. First, attention is paid to the spatial area related to the foreground, and compared with other channels, more attention is given to the feature channel with a greater response to the foreground region. Second, for objects with different scales, MSCA can generate an attention distribution map that integrates multi-scale information and applies it to the feature map of the deep network. MSCA is a flexible module that can be easily embedded into any object detection model based on deep learning. With the attention exerted by MSCA, the deep neural network can efficiently focus on objects of different backgrounds and sizes in remote sensing images. Experiments show that the mean average precision of object detection is improved after the addition of MSCA to the current object detection model.

中文翻译：

---

用于改进遥感图像中目标检测的多尺度空间和通道注意

随着遥感卫星和传感器技术的发展，遥感图像的空间分辨率不断提高。因此，图像中的背景信息变得越来越复杂，并对目标检测任务造成相当大的干扰。我们能否像人类视觉一样关注图像中的物体？这封信提出了一种多尺度空间和通道注意（MSCA）机制来回答这个问题。MSCA 具有两个有助于提高对象检测性能的优势。首先，关注与前景相关的空间区域，与其他通道相比，更多关注对前景区域响应更大的特征通道。其次，对于不同尺度的物体，MSCA 可以生成集成多尺度信息的注意力分布图，并将其应用于深度网络的特征图。MSCA 是一个灵活的模块，可以轻松嵌入到任何基于深度学习的对象检测模型中。通过MSCA的注意力，深度神经网络可以有效地聚焦遥感图像中不同背景和大小的物体。实验表明，在现有的物体检测模型中加入MSCA后，物体检测的平均精度得到了提高。深度神经网络可以有效地聚焦遥感图像中不同背景和大小的物体。实验表明，在现有的物体检测模型中加入MSCA后，物体检测的平均精度得到了提高。深度神经网络可以有效地聚焦遥感图像中不同背景和大小的物体。实验表明，在现有的物体检测模型中加入MSCA后，物体检测的平均精度得到了提高。