论文标题
基于多尺度卷积网络的高分辨率遥感图像中的区域感知旋转的船舶检测
Locality-Aware Rotated Ship Detection in High-Resolution Remote Sensing Imagery Based on Multi-Scale Convolutional Network
论文作者
论文摘要
十年来，船舶检测一直是遥感领域的一个积极而重要的话题，但由于大规模变化，高纵横比，强化安排和背景混乱的干扰，这仍然是一个具有挑战性的问题。在这封信中，我们提出了一个基于多尺度卷积神经网络（CNN）的地方感知的旋转船舶检测（LARSD）框架，以解决这些问题。所提出的框架应用了一个类似于UNET的多尺度CNN来生成具有高分辨率的高级语义信息的多尺度特征图。然后，将基于旋转的锚回归用于直接预测船舶的概率，边缘距离和船舶角。最后，提出了一个局部感知的分数比对来固定分类结果与由每个子网独立性引起的位置结果之间的不匹配。此外，为了扩大船舶检测数据集，我们构建了一个新的高分辨率船舶检测（HRSD）数据集，从Google Earth收集了2499张图像和9269个实例，并以不同的分辨率收集。基于公共数据集HRSC2016和我们的HRSD数据集的实验表明，我们的检测方法可实现最先进的性能。
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.
