HRSC2016

Extracting ships from complex backgrounds is the bottleneck of ship detection in high-resolution optical satellite images. In this letter, we propose a nearly closed-form ship rotated bounding box space used for ship detection and design a method to generate a small number of highly potential candidates based on this space. We first analyze the possibility of accurately covering all ships by labeling rotated bounding boxes. Moreover, to reduce search space, we construct a nearly closed-form ship rotated bounding box space. Then, by scoring for each latent candidate in the space using a two-cascaded linear model followed by binary linear programming, we select a small number of highly potential candidates. Moreover, we also propose a fast version of our method. Experiments on our data set validate the effectiveness of our method and the efficiency of its fast version, which achieves a close detection rate in near real time.

从复杂背景中提取船舶，是高分辨率光学卫星图像中船舶检测的瓶颈问题。本通讯中，我们提出一种适用于船舶检测的近似闭式（nearly closed-form）船舶旋转边界框（rotated bounding box）空间，并基于该空间设计了一种可生成少量高潜力候选框的方法。我们首先分析了通过标注旋转边界框以精准覆盖全部船舶的可行性。此外，为压缩搜索空间，我们构建了该近似闭式船舶旋转边界框空间。随后，通过两级级联线性模型（two-cascaded linear model）对空间内的各潜在候选框进行评分，再结合二进制线性规划（binary linear programming），筛选出少量高潜力候选框。此外，我们还提出了该方法的快速版本。在我们的数据集上开展的实验验证了所提方法的有效性及其快速版本的高效性，该快速版本可实现接近实时的高检测率。

搜集汇总

背景与挑战

背景概述

HRSC2016是一个专门用于船舶检测的航空影像数据集，包含1,061张标注图像，覆盖28个细粒度船舶类别，适用于高分辨率光学卫星图像中的船舶检测研究。