Automatic detection of multi-class objects in remote sensing images is a fundamental but challenging problem faced for remote sensing image analysis. Traditional methods are based on hand-crafted or shallow-learning-based features with limited representation power. Recently, deep learning algorithms, especially Faster region based convolutional neural networks (FRCN), has shown their much stronger detection power in computer vision field. However, several challenges limit the applications of FRCN in multi-class objects detection from remote sensing images: (1) Objects often appear at very different scales in remote sensing images, and FRCN with a fixed receptive field cannot match the scale variability of different objects; (2) Objects in large-scale remote sensing images are relatively small in size and densely peaked, and FRCN has poor localization performance with small objects; (3) Manual annotation is generally expensive and the available manual annotation of objects for training FRCN are not sufficient in number. To address these problems, this paper proposes a unified and effective method for simultaneously detecting multi-class objects in remote sensing images with large scales variability. Firstly, we redesign the feature extractor by adopting Concatenated ReLU and Inception module, which can increases the variety of receptive field size. Then, the detection is preformed by two sub-networks: a multi-scale object proposal network (MS-OPN) for object-like region generation from several intermediate layers, whose receptive fields match different object scales, and an accurate object detection network (AODN) for object detection based on fused feature maps, which combines several feature maps that enables small and densely packed objects to produce stronger response. For large-scale remote sensing images with limited manual annotations, we use cropped image blocks for training and augment them with re-scalings and rotations. The quantitative comparison results on the challenging NWPU VHR-10 data set, aircraft data set, Aerial-Vehicle data set and SAR-Ship data set show that our method is more accurate than existing algorithms and is effective for multi-modal remote sensing images.

遥感图像中多类物体的自动检测是遥感图像分析面临的一个基本但具有挑战性的问题。传统方法基于具有有限表示能力的手工或基于浅层学习的功能。最近，深度学习算法，尤其是基于Faster Region的卷积神经网络（FRCN），已显示出它们在计算机视觉领域的强大检测能力。但是，一些挑战限制了FRCN在遥感图像的多类物体检测中的应用：（1）物体在遥感图像中通常以非常不同的比例出现，并且具有固定接收场的FRCN无法匹配不同物体的比例可变性; （2）大型遥感影像中的物体相对较小，并且具有密集的峰值，FRCN的小物体定位性能较差；（3）手动注释通常很昂贵，并且用于训练FRCN的对象的可用手动注释数量不足。针对这些问题，本文提出了一种统一且有效的方法，可以同时检测出具有较大尺度变化的遥感图像中的多类物体。首先，我们通过采用级联的ReLU和Inception模块重新设计特征提取器，这可以增加接收场大小的多样性。然后，通过两个子网进行检测：一个多尺度目标提议网络（MS-OPN），用于从多个中间层（其接收场匹配不同目标尺度）生成目标区域，以及一个精确的目标检测网络（ AODN）用于基于融合特征图的目标检测，结合了多个特征图，这些特征图使较小且密集的对象能够产生更强的响应。对于手动注释有限的大型遥感图像，我们使用裁剪的图像块进行训练，并通过重新缩放和旋转来增强它们。在具有挑战性的NWPU VHR-10数据集，飞机数据集，航空车辆数据集和SAR-Ship数据集上的定量比较结果表明，我们的方法比现有算法更准确，并且对于多模式遥感影像是有效的。