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核心瓶颈。本研究快报中，我们提出了一种适用于船舶检测的近似闭式船舶旋转边界框（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，我们借助双级线性模型（two-cascaded linear model）对该空间内的各潜在候选框进行评分，并结合二元线性规划（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变体版本。在我们自建的数据集上开展的实验验证了所提方法的有效性，以及其快速变体的高效性，该变体可在近实时场景下实现相近的检测率。

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，涵盖28个细粒度船舶类别，适用于人工智能和深度学习在遥感领域的应用。