Object detection from satellite images is challenging and either computationally expensive or labor intense. Satellite images often cover large areas of more than $10km\times 10km$. They include objects of different scales, which makes it hard to detect all of them at the same image resolution. Considering that airplanes are usually located in airports, ships are often distributed in ports and sea areas, and that oil depots are typically found close to airports or ports, we propose a new hierarchical object detection framework for very high-resolution satellite images. Our framework prescribes two stages: (1) detecting airports and ports in down-sampled satellite images and (2) mapping the detected object back to the original high-resolution satellite images for detecting the smaller objects near them. In order to improve the efficiency of object detection, we further propose a contextual information based deep feature extraction approach for both of the hierarchical detection steps, as well as an inclined bounding box based arbitrarily-oriented object location mechanism suitable especially for the smaller objects. Comprehensive experiments on a public dataset and two self-assembled datasets (which we made publicly available) show the superior performance of our method compared to standalone state-of-the-art object detectors.

从卫星图像中检测目标具有挑战性，要么计算成本高，要么劳动强度大。卫星图像通常覆盖超过$10克米\times 10克米$. 它们包括不同比例的对象，这使得很难以相同的图像分辨率检测所有这些对象。考虑到飞机通常位于机场，船舶通常分布在港口和海域，并且油库通常位于机场或港口附近，我们提出了一种新的超高分辨率卫星图像分层目标检测框架。我们的框架规定了两个阶段：（1）检测下采样卫星图像中的机场和港口；（2）将检测到的物体映射回原始高分辨率卫星图像，以检测它们附近的较小物体。为了提高目标检测的效率，我们进一步为两个层次检测步骤提出了一种基于上下文信息的深度特征提取方法，以及基于倾斜边界框的任意面向对象定位机制，特别适用于较小的对象。在公共数据集和两个自组装数据集（我们公开提供）上的综合实验表明，与独立的最先进的对象检测器相比，我们的方法具有优越的性能。