Object detection plays an important role in the field of remote sensing imagery analysis. The most challenging issues in advancing this task are the large variation in object scales and the arbitrary orientation of objects. In this paper, we build a unified framework upon the region-based convolutional neural network for arbitrary-oriented and multi-scale object detection in remote sensing images. To handle the problem of multi-scale object detection, a feature-fusion architecture is proposed to generate a multi-scale feature hierarchy, which augments the features of shallow layers with semantic representations via a top-down pathway and combines the feature maps of top layers with low-level information by a bottom-up pathway. By combining features of different levels, we can form a powerful feature representation for multi-scale objects. Most previous methods locate objects with arbitrary orientations and dense spatial distributions via axis-aligned boxes, which may cover adjacent instances and background areas. We build a rotation-aware object detector that uses oriented boxes to localize objects in remote sensing images. The region proposal network augments the anchors with multiple default angles to cover oriented objects. It utilizes oriented proposal boxes to enclose objects rather than horizontal proposals that coarsely locate oriented objects. The orientation RoI pooling operation is introduced to extract the feature maps of oriented proposals for the following R-CNN subnetwork. We conduct comprehensive experiments on a public dataset for oriented object detection in remote sensing images. Our method achieves state-of-the-art performance, which demonstrates the effectiveness of the proposed methods.

目标检测在遥感影像分析领域中起着重要作用。推进此任务中最具挑战性的问题是对象比例的巨大差异和对象的任意方向。在本文中，我们基于基于区域的卷积神经网络构建了一个统一的框架，用于遥感图像中任意方向和多尺度目标的检测。为了解决多尺度目标检测的问题，提出了一种特征融合架构来生成多尺度特征层次结构，该层次结构通过自上而下的路径用语义表示来增强浅层的特征，并结合顶部的特征图。通过自下而上的途径获得具有低级信息的层。通过组合不同级别的特征，我们可以为多尺度对象形成强大的特征表示。大多数以前的方法都是通过轴对齐的框来定位具有任意方向和密集空间分布的对象，这些框可能会覆盖相邻的实例和背景区域。我们构建了一个旋转感知的对象检测器，该对象检测器使用定向框在遥感图像中定位对象。区域提议网络使用多个默认角度扩展锚点，以覆盖定向的对象。它利用定向建议框围住对象，而不是粗略定位定向对象的水平建议。引入了定向RoI池操作，以提取后续R-CNN子网的定向提议的特征图。我们在公共数据集上进行了全面的实验，用于遥感图像中定向目标的检测。我们的方法达到了最先进的性能，