无人机实时检测高光谱异常，快速定位可疑区域。
A real-time UAS hyperspectral anomaly detection system
- 在小型无人机上实时运行异常检测算法，边飞边分析
- 生成的异常信息比原始数据小得多，可无线传输
- 支持地面站即时查看与交互，适合应急巡查场景
高光谱图像中的异常检测（即与背景光谱明显不同的区域）是常见任务，可能代表对操作员有重要意义的目标。但传统方法需事后处理，延迟洞察。为此，本文在小型无人航空系统（UAS）上实时部署可见光与近红外（VNIR）推扫式高光谱传感器的异常检测算法，研究平台限制对算法的影响。由于生成的异常信息远小于原始数据，可实现无线传输。结合创新的快速地理校正算法，使地面站接收者能立即交互式地调查和分析异常区域。该方案实现了从数据采集、预处理、异常检测、传输到地面显示与交互的全链路实时处理，使用相对低成本组件完成端到端应用。
原文摘要 · Abstract (English)
Detecting anomalies in hyperspectral image data, i.e. regions which are spectrally distinct from the image background, is a common task in hyperspectral imaging. Such regions may represent interesting objects to human operators, but obtaining results often requires post-processing of captured data, delaying insight. To address this limitation, we apply an anomaly detection algorithm to a visible and near-infrared (VNIR) push-broom hyperspectral image sensor in real time onboard a small uncrewed aerial system (UAS), exploring how UAS limitations affect the algorithm. As the generated anomaly information is much more concise than the raw hyperspectral data, it can feasibly be transmitted wirelessly. To detection, we couple an innovative and fast georectification algorithm that enables anomalous areas to be interactively investigated and characterized immediately by a human operator receiving the anomaly data at a ground station. Using these elements, we demonstrate a novel and complete end-to-end solution from data capture and preparation, through anomaly detection and transmission, to ground station display and interaction, all in real time and with relatively low cost components.
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