Satellite imagery, due to its long-range imaging, brings with it a variety of scale-preferred tasks, such as the detection of tiny/small objects, making the precise localization and detection of small objects of interest a challenging task. In this article, we design a Knowledge Discovery Network (KDN) to implement the renormalization group theory in terms of efficient feature extraction. Renormalized connection (RC) on the KDN enables ``synergistic focusing'' of multi-scale features. Based on our observations of KDN, we abstract a class of RCs with different connection strengths, called n21C, and generalize it to FPN-based multi-branch detectors. In a series of FPN experiments on the scale-preferred tasks, we found that the ``divide-and-conquer'' idea of FPN severely hampers the detector's learning in the right direction due to the large number of large-scale negative samples and interference from background noise. Moreover, these negative samples cannot be eliminated by the focal loss function. The RCs extends the multi-level feature's ``divide-and-conquer'' mechanism of the FPN-based detectors to a wide range of scale-preferred tasks, and enables synergistic effects of multi-level features on the specific learning goal. In addition, interference activations in two aspects are greatly reduced and the detector learns in a more correct direction. Extensive experiments of 17 well-designed detection architectures embedded with n21s on five different levels of scale-preferred tasks validate the effectiveness and efficiency of the RCs. Especially the simplest linear form of RC, E421C performs well in all tasks and it satisfies the scaling property of RGT. We hope that our approach will transfer a large number of well-designed detectors from the computer vision community to the remote sensing community.

翻译：卫星图像因其远距离成像特性，带来了多种尺度偏好任务，例如微小/小目标检测，这使得对感兴趣小目标的精确定位与检测成为一项具有挑战性的任务。本文中，我们设计了一个知识发现网络（KDN），以高效特征提取的方式实现重归一化群理论。KDN上的重归一化连接（RC）能够实现多尺度特征的“协同聚焦”。基于对KDN的观察，我们抽象出一类具有不同连接强度的RC，称为n21C，并将其推广到基于FPN的多分支检测器中。在一系列针对尺度偏好任务的FPN实验中，我们发现，由于存在大量大尺度负样本以及背景噪声的干扰，FPN的“分而治之”思想严重阻碍了检测器朝正确方向学习。此外，这些负样本无法通过焦点损失函数消除。RC将基于FPN的检测器的多层级特征“分而治之”机制扩展到了广泛的尺度偏好任务中，并使多层级特征在特定学习目标上产生协同效应。此外，两个方面的干扰激活被大幅减少，检测器得以沿更正确的方向学习。在五个不同等级的尺度偏好任务上，对17种嵌入n21C的精心设计的检测架构进行的广泛实验验证了RC的有效性和效率。特别是最简单的RC线性形式E421C在所有任务中均表现良好，且满足RGT的标度特性。我们希望我们的方法能将大量计算机视觉领域精心设计的检测器迁移到遥感领域。