DarkVesselNet: 用于暗船检测的多模态遥感和轨迹推理
DarkVesselNet: Multi-Modal Remote Sensing and Trajectory Reasoning for Dark Vessel Detection
AI总结：
提出DarkVesselNet，融合Sentinel-1 SAR、Sentinel-2光学影像、地理空间基础模型、AIS轨迹推理、TGARD间隙检测和Pi-DPM异常头，实现多模态遥感暗船检测。
摘要：
暗船检测需要融合船只通过AIS报告的信息与卫星通过雷达和光学传感器观测到的信息。DarkVesselNet是一个多模态遥感堆栈，结合了Sentinel-1 SAR、Sentinel-2光学影像、地理空间基础模型骨干、AIS轨迹推理、TGARD风格的间隙检测以及受Pi-DPM启发的异常头。该仓库将系统呈现为经过测试的Python包和公开的Hugging Face Space。本文介绍了传感器堆栈、骨干抽象、融合路径、异常头和当前的验证。目前可用的证据是基于软件的：针对SAR散斑滤波、光学波段比、Haversine距离、TGARD间隙发射、传感器配准、骨干token形状和可微分异常评分的测试。
英文摘要：
Dark vessel detection requires fusing what vessels report through AIS with what satellites observe through radar and optical sensors. DarkVesselNet is a multi-modal remote sensing stack that combines Sentinel-1 SAR, Sentinel-2 optical imagery, geospatial foundation model backbones, AIS trajectory reasoning, TGARD-style gap detection, and a Pi-DPM-inspired anomaly head. The repository exposes the system as a tested Python package and a public Hugging Face Space. The paper presents the sensor stack, backbone abstraction, fusion path, anomaly head, and current validation. The evidence currently available is software-grounded: tests for SAR speckle filtering, optical band ratios, Haversine distance, TGARD gap emission, sensor coregistration, backbone token shapes, and differentiable anomaly scoring.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
