Robust Cloud Suppression and Anomaly Detection in Time-Lapse Thermography
Robust Cloud Suppression and Anomaly Detection in Time-Lapse Thermography
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DOI：
10.3390/rs16020255
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发表时间：
2023-11
期刊：
影响因子：
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通讯作者：
Christopher Small;D. Sousa
中科院分区：
文献类型：
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作者：
Christopher Small;D. Sousa
由于其短暂的性质，云代表相对于感兴趣的底层景观的异常。因此，云识别的挑战可以被认为是异常检测的更一般问题中的一个特定情况。瞬变异常的混杂效应是陆面过程时空分析中特别棘手的问题。虽然时空表征提供了一个统计基础，以量化最显着的时间模式和它们的空间分布，而不需要先验假设所观察到的变化，瞬态异常的存在下，可以掩盖感兴趣的时空过程的统计特性。本研究的目的是实施和评估一个强大的方法来区分云和其他瞬变异常的昼夜和年度的热循环与时间推移热成像观察。该方法使用鲁棒主成分分析（RPCA），以统计区分低秩（L）和稀疏（S）的陆地表面温度图像时间序列的组成部分，其次是其低秩分量的时空特征，以量化的主导日和年热循环在研究区域。RPCA有效地隔离云，传感器异常，测绘带间隙，地理空间位移和瞬态热异常到稀疏分量时间序列。低秩分量时间序列的时空特征清楚地解决了各种不同的土地覆盖和水体的昼夜和年度热循环，同时隔离可能感兴趣的瞬态异常。
Due to their transient nature, clouds represent anomalies relative to the underlying landscape of interest. Hence, the challenge of cloud identification can be considered a specific case in the more general problem of anomaly detection. The confounding effects of transient anomalies are particularly troublesome for spatiotemporal analysis of land surface processes. While spatiotemporal characterization provides a statistical basis to quantify the most significant temporal patterns and their spatial distributions without the need for a priori assumptions about the observed changes, the presence of transient anomalies can obscure the statistical properties of the spatiotemporal processes of interest. The objective of this study is to implement and evaluate a robust approach to distinguish clouds and other transient anomalies from diurnal and annual thermal cycles observed with time-lapse thermography. The approach uses Robust Principal Component Analysis (RPCA) to statistically distinguish low-rank (L) and sparse (S) components of the land surface temperature image time series, followed by a spatiotemporal characterization of its low rank component to quantify the dominant diurnal and annual thermal cycles in the study area. RPCA effectively segregates clouds, sensor anomalies, swath gaps, geospatial displacements and transient thermal anomalies into the sparse component time series. Spatiotemporal characterization of the low-rank component time series clearly resolves a variety of diurnal and annual thermal cycles for different land covers and water bodies while segregating transient anomalies potentially of interest.