For the past two decades, there have been significant efforts to develop methods for object detection in Remote Sensing (RS) images. In most cases, the datasets for small object detection in remote sensing images are inadequate. Many researchers used scene classification datasets for object detection, which has its limitations; for example, the large-sized objects outnumber the small objects in object categories. Thus, they lack diversity; this further affects the detection performance of small object detectors in RS images. This paper reviews current datasets and object detection methods (deep learning-based) for remote sensing images. We also propose a large-scale, publicly available benchmark Remote Sensing Super-resolution Object Detection (RSSOD) dataset. The RSSOD dataset consists of 1,759 hand-annotated images with 22,091 instances of very high resolution (VHR) images with a spatial resolution of ~0.05 m. There are five classes with varying frequencies of labels per class. The image patches are extracted from satellite images, including real image distortions such as tangential scale distortion and skew distortion. We also propose a novel Multi-class Cyclic super-resolution Generative adversarial network with Residual feature aggregation (MCGR) and auxiliary YOLOv5 detector to benchmark image super-resolution-based object detection and compare with the existing state-of-the-art methods based on image super-resolution (SR). The proposed MCGR achieved state-of-the-art performance for image SR with an improvement of 1.2dB PSNR compared to the current state-of-the-art NLSN method. MCGR achieved best object detection mAPs of 0.758, 0.881, 0.841, and 0.983, respectively, for five-class, four-class, two-class, and single classes, respectively surpassing the performance of the state-of-the-art object detectors YOLOv5, EfficientDet, Faster RCNN, SSD, and RetinaNet.


翻译:在过去20年中,为开发遥感图像中天体探测方法做出了重大努力;在多数情况下,遥感图像中小物体探测的数据集不够;许多研究人员使用现场分类数据集进行天体探测,这有其局限性;例如,大型天体数量超过物体类别中的小天体数量;因此,它们缺乏多样性;这进一步影响到RS图像中小物体探测器的探测性能;本文审查了遥感图像中的当前数据集和天体探测方法(深入学习基础);我们还提出了大规模、公开的遥感超分辨率探测基准数据集;许多研究人员使用现场分类数据集进行天体探测,这些数据集有1,759个手标的图像,有22,091个非常高的天体分辨率;因此,它们缺乏多样性;这进一步影响到RS图像在每类中小物体探测器的频率不同;本文审查了遥感图像的当前数据集和物体探测方法(深度变异形)等真实图像扭曲;我们还提出了一个新的多级天体遥感超分辨率探测超分辨率标标标标标的超分辨率标点点点点点58(以现有磁标的SLMRM-S-GRMMM-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-R-Sl-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S

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