Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product
University of California - Los Angeles（加利福尼亚大学洛杉矶分校）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Joint Institute for Regional Earth System Science and Engineering, University of California - Los Angeles（加利福尼亚大学洛杉矶分校区域地球系统科学与工程联合研究所）
Harris Hardiman-Mostow, Charles Marshak, Alexander L. Handwerger
本文基于 OPERA RTC-S1 数据训练无需标签的自监督 vision transformer，通过逐像素基线分布偏差检测地表扰动，并在三地灾害事件中取得优于现有方法的结果。
摘要：
绘制地表扰动图可为灾害响应、资源与生态系统管理以及气候适应工作提供支持。合成孔径雷达（SAR）是扰动制图的宝贵工具，能够不受天气或光照条件影响，提供一致的地面时间序列影像。尽管 SAR 在扰动制图方面具有潜力，但将 SAR 数据处理为可直接分析的格式需要专业知识和大量计算资源，对于大规模全球分析而言尤其如此。2023 年 10 月，NASA 的“遥感分析面向终端用户的观测产品”（Observational Products for End-Users from Remote Sensing Analysis，OPERA）项目发布了来自 Sentinel-1 的近全球辐射地形校正 SAR 后向散射（RTC-S1）数据集，提供公开可用、可直接分析的 SAR 影像。在本研究中，我们利用这一新数据集系统分析地表扰动。由于标注 SAR 数据往往极其耗时，我们在 OPERA RTC-S1 数据上训练了一个自监督 vision transformer——该模型训练不需要标签——以根据基线影像集合估计逐像素分布，并在观测显著偏离建模分布时评估扰动。为测试模型的能力和通用性，我们评估了来自世界三个不同地区的三种不同自然灾害，它们代表高强度、突发性扰动。在各个事件中，我们的方法都产生了高质量勾绘结果：F1 分数超过 0.6，精确率-召回率曲线下面积超过 0.65，并持续优于现有 SAR 扰动方法。我们的研究结果表明，自监督 vision transformer 非常适合全球扰动制图，并且能够成为业务化近全球扰动监测的宝贵工具，尤其是在不存在标注数据时。
英文摘要：
Mapping land surface disturbances supports disaster response, resource and ecosystem management, and climate adaptation efforts. Synthetic aperture radar (SAR) is an invaluable tool for disturbance mapping, providing consistent time-series images of the ground regardless of weather or illumination conditions. Despite SAR's potential for disturbance mapping, processing SAR data to an analysis-ready format requires expertise and significant compute resources, particularly for large-scale global analysis. In October 2023, NASA's Observational Products for End-Users from Remote Sensing Analysis (OPERA) project released the near-global Radiometric Terrain Corrected SAR backscatter from Sentinel-1 (RTC-S1) dataset, providing publicly available, analysis-ready SAR imagery. In this work, we utilize this new dataset to systematically analyze land surface disturbances. As labeling SAR data is often prohibitively time-consuming, we train a self-supervised vision transformer - which requires no labels to train - on OPERA RTC-S1 data to estimate a per-pixel distribution from the set of baseline imagery and assess disturbances when there is significant deviation from the modeled distribution. To test our model's capability and generality, we evaluate three different natural disasters - which represent high-intensity, abrupt disturbances - from three different regions of the world. Across events, our approach yields high quality delineations: F1 scores exceeding 0.6 and Areas Under the Precision-Recall Curve exceeding 0.65, consistently outperforming existing SAR disturbance methods. Our findings suggest that a self-supervised vision transformer is well-suited for global disturbance mapping and can be a valuable tool for operational, near-global disturbance monitoring, particularly when labeled data does not exist.
University of Oxford（牛津大学）；University of Valencia（瓦伦西亚大学）
High resolution remote sensing imagery is used in broad range of tasks, including detection and classification of objects. High-resolution imagery is however expensive, while lower resolution imagery is often freely available and can be used by the public for range of social good applications. To that end, we curate a multi-spectral multi-image super-resolution dataset, using PlanetScope imagery from the SpaceNet 7 challenge as the high resolution reference and multiple Sentinel-2 revisits of the same imagery as the low-resolution imagery. We present the first results of applying multi-image s…
NOVA-LINCS；Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa（里斯本新大学科学与技术学院）
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