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Robust Cloud Suppression and Anomaly Detection in Time-lapse Thermography
Geophysics · Submitted 28 Nov 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The RPCA effectively segregated clouds, sensor anomalies, swath gaps, geospatial displacements and transient thermal anomalies into the sparse component time series.
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The abstract, sentence by sentence
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. 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 form, amplitude or timing of the observed changes. 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 and sparse components of the land surface temperature image time series, followed by a spatiotemporal characterization of the L component time series to quantify the dominant diurnal and annual thermal cycles in the study area. The RPCA effectively segregated 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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Due to their transient nature, clouds represent anomalies relative to the underlying landscape of interest. | background 1.00 | 0.10 | 0.03 | asserted 1.00 |
| s1 | Hence, the challenge of cloud identification can be considered a specific case in the more general problem of anomaly detection. | background 0.99 | 0.09 | 0.03 | asserted 0.73 |
| s2 | The confounding effects of transient anomalies are particularly troublesome for spatiotemporal analysis of land surface processes. | background 0.99 | 0.06 | 0.02 | asserted 1.00 |
| s3 | 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 form, amplitude or timing of the observed changes. | background 0.59 | 0.12 | 0.04 | asserted 1.00 |
| s4 | 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. | contribution 0.99 | 0.54 | 0.05 | asserted 0.81 |
| s5 | The approach uses Robust Principal Component Analysis (RPCA) to statistically distinguish low rank and sparse components of the land surface temperature image time series, followed by a spatiotemporal characterization of the L component time series to quantify the dominant diurnal and annual thermal cycles in the study area. | method 1.00 | 0.87 | 0.11 | asserted 1.00 |
| s6 | The RPCA effectively segregated clouds, sensor anomalies, swath gaps, geospatial displacements and transient thermal anomalies into the sparse component time series. | result 1.00 | 0.93 | 0.42 | asserted 1.00 |
| s7 | 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. | result 1.00 | 0.92 | 0.27 | asserted 1.00 |
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The paper
Original abstract
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. 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 form, amplitude or timing of the observed changes. 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 and sparse components of the land surface temperature image time series, followed by a spatiotemporal characterization of the L component time series to quantify the dominant diurnal and annual thermal cycles in the study area. The RPCA effectively segregated 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.
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