发表机构
Southeast University; Univ. Grenoble Alpes; INRAE; Inria; CNRS; Grenoble INP（东南大学; 格勒诺布尔大学; 法国国家农业、食品与环境研究院; 法国国家信息与自动化研究所; 法国国家科学研究中心; 格勒诺布尔理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有遥感数据集仅关注光学单模态且难以应对复杂海况的问题，提出全球最大规模的多模态船舶感知基准VesselBench-800K，含80万张光学与SAR图像，支持检测、计数和密度估计，并评估了多种先进模型。
AI 中文摘要
从太空进行船舶感知对于广泛的海事应用至关重要，从交通监控到环境保护。然而，现有的大多数数据集主要关注光学遥感（RS）图像中的通用目标检测任务。仅依赖单一模态的光学遥感图像，在复杂的海上场景中无法有效感知船舶目标，因为多变的天气条件（如云和雨）、昼夜覆盖的需求以及单一成像模态的固有局限性构成了重大挑战。为填补这一空白，我们引入了VesselBench-800K，这是迄今为止全球规模最大的用于多模态遥感图像中船舶感知的基准数据集。顾名思义，VesselBench-800K包含80万张图像，每张分辨率为512x512像素，专门为船舶感知任务（如检测、计数和密度估计）而策划。这些多模态图像对（即光学、SAR）收集自不同的平台、传感器、场景、拍摄高度和合成来源，空间分辨率从4.5米到0.1米不等。此外，我们通过定性和定量比较，在VesselBench-800K上评估了众多最先进的检测、计数和密度估计模型。通过揭示以前未被识别的线索，该数据集具有巨大潜力，可显著推进我们对海上交通的理解。我们的VesselBench数据集将在https URL上公开提供，以支持和促进社区发展。
英文摘要
Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most existing datasets predominantly focus on general object detection tasks in optical remote sensing (RS) images. Relying solely on single-modality optical RS images proves inadequate for effectively perceiving vessel objects in complex maritime scenarios, where ever-changing weather conditions (e.g., clouds and rain), the need for day-and-night coverage, and the inherent limitations of a single imaging modality pose significant challenges. To fill this gap, we introduce VesselBench-800K, the largest-to-date benchmark dataset on a global scale for vessel perception in multimodal RS images. As its name suggests, VesselBench-800K comprises 800,000 images, each at a resolution of 512x512 pixels, specifically curated for vessel perception tasks such as detection, counting, and density estimation. These multimodal image pairs (i.e., optical, SAR) are collected from diverse platforms, sensors, scenes, shooting heights, and synthetic sources, spanning spatial resolutions from 4.5m to 0.1m. Furthermore, we evaluate numerous state-of-the-art detection, counting, and density estimation models on VesselBench-800K through both qualitative and quantitative comparisons. By revealing previously unrecognized cues, this dataset holds immense potential to significantly advance our understanding of marine traffic. Our VesselBench dataset will be publicly available at https://github.com/danfenghong/IEEE_TGRS_VesselBench to support and contribute to community development.
