发表机构
University of Texas at El Paso; Colorado State University（德克萨斯大学埃尔帕索分校; 科罗拉多州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对卫星降水数据验证缺口，提出传感器自适应增量学习框架，结合预训练计算机视觉模型与少量标注数据检测伪影，在SSMI、SSMIS数据上表现优异且可迭代优化。
AI 中文摘要
从卫星影像中获取降雨数据历来是航天机构的专属领域，但近年来，能够检测降雨代理的更廉价、更紧凑的卫星（SmallSats）的发展，催生了大量私营部门的卫星发射和地表降水产品项目，这种快速增长尚未得到数据验证工作的匹配，因此，在近实时数据向公众发布前，亟需一套强大的工具来检测其中的异常。本文开发了一套异常检测系统，用于识别全球卫星基降雨产品中的伪影，该框架利用预训练的计算机视觉模型，并结合稀缺的人工标注数据来检测特定异常；所提出的异常检测策略在专用传感器微波成像仪（SSMI）和专用传感器微波成像仪/探测仪（SSMIS）的数据上进行了测试，结果表明，该方法能有效区分各卫星的常规轨道与含伪影轨道，性能可与最先进的原位方法相媲美，此外，该框架具备可解释性，且能在出现误判（假阳性或假阴性）后进行迭代优化。
英文摘要
Historically, retrieving rainfall data from satellite imagery has been the domain of space agencies. However, in recent years, the development of cheaper, more compact satellites (SmallSats) capable of detecting rainfall proxies has led to a significant increase in private-sector initiatives for satellite launch and surface precipitation products. This rapid growth has yet to be matched by data validation efforts. Consequently, the need for a robust tool to detect anomalies in near-real-time data before it is disseminated to the public has become critical. In this paper, we present the development of an anomaly-detection system to identify artifacts in global satellite-based rainfall products. The developed framework leverages pre-trained computer vision models and incorporates scarce human-labeled data to detect specific anomalies. Our proposed anomaly detection strategy is tested on data from the Special Sensor Microwave Imager (SSMI) and the Special Sensor Microwave Imager/Sounder (SSMIS). Results demonstrate the efficacy of our approach at separating regular orbits from artifact-containing orbits for each satellite, with performance comparable to state-of-the-art in-place methods. Additionally, the framework offers explainability and the capacity for iterative refinement following false-positive or false-negative classifications.
Comments16 pages. Submitted to IEEE Transactions on Geoscience and Remote Sensing
