Ship detection has been an active and vital topic in the field of remote sensing for a decade, but it is still a challenging problem due to the large scale variations, the high aspect ratios, the intensive arrangement, and the background clutter disturbance. In this letter, we propose a locality-aware rotated ship detection (LARSD) framework based on a multi-scale convolutional neural network (CNN) to tackle these issues. The proposed framework applies a UNet-like multi-scale CNN to generate multi-scale feature maps with high-level semantic information in high resolution. Then, a rotated anchor-based regression is applied for directly predicting the probability, the edge distances, and the angle of ships. Finally, a locality-aware score alignment is proposed to fix the mismatch between classification results and location results caused by the independence of each subnet. Furthermore, to enlarge the datasets of ship detection, we build a new high-resolution ship detection (HRSD) dataset, where 2499 images and 9269 instances were collected from Google Earth with different resolutions. Experiments based on public dataset HRSC2016 and our HRSD dataset demonstrate that our detection method achieves state-of-the-art performance.
翻译:过去十年来,在遥感领域,船舶探测是一个活跃和重要的专题,但由于大规模变化、高方比率、密集安排和背景混乱,这仍然是一个具有挑战性的问题。在本信中,我们提议以多规模的革命神经网络为基础,建立一个符合地貌的轮用船舶探测框架来解决这些问题。拟议框架采用类似UNet的多级CNN来制作具有高分辨率高语义信息的多比例地貌图。然后,对直接预测船只概率、边缘距离和角度采用旋转锚基回归法。最后,建议对地貌觉测得分进行比对,以修正分类结果与每个子网独立造成的位置结果之间的不匹配。此外,为了扩大船舶探测的数据集,我们建立了一个新的高分辨率船舶探测数据集,其中用不同分辨率从谷歌地球收集了2499个图像和9269个案例。基于公共数据集HRSC2016的实验和我们的HRSD数据显示我们的检测方法达到状态。