卫星自动识别火山喷发并规划拍摄轨迹，提升高精度观测效率。
Real-Time Instrument Planning and Perception for Novel Measurements of Dynamic Phenomena
- 结合视觉检测与自主路径规划，实时追踪火山喷发
- 仿真显示高分辨率仪器利用率提升10倍以上
- 适合遥感、灾害监测领域研究人员参考
随着机载计算能力的提升，远程感知设备可在边缘端应用先进的计算机视觉与机器学习技术。本文提出一种自动化工作流，将前瞻卫星图像中动态科学现象的检测与后续高分辨率传感器的自主轨迹规划相结合，实现对瞬时事件的精准测量。以火山喷发为例，对比传统机器学习与卷积神经网络的分类方法，设计多种基于喷发形态特征跟踪的轨迹规划算法，并与分类器集成。仿真结果表明，相比基线方法，高分辨率仪器的效用回报提升一个数量级，同时保持高效运行时间。
原文摘要 · Abstract (English)
Advancements in onboard computing mean remote sensing agents can employ state-of-the-art computer vision and machine learning at the edge. These capabilities can be leveraged to unlock new rare, transient, and pinpoint measurements of dynamic science phenomena. In this paper, we present an automated workflow that synthesizes the detection of these dynamic events in look-ahead satellite imagery with autonomous trajectory planning for a follow-up high-resolution sensor to obtain pinpoint measurements. We apply this workflow to the use case of observing volcanic plumes. We analyze classification approaches including traditional machine learning algorithms and convolutional neural networks. We present several trajectory planning algorithms that track the morphological features of a plume and integrate these algorithms with the classifiers. We show through simulation an order of magnitude increase in the utility return of the high-resolution instrument compared to baselines while maintaining efficient runtimes.
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