YA-LUN TSAIHuang, Chun JiaChun JiaHuangChen, Chia LingChia LingChenJEN-YU HAN2024-01-042024-01-042023-01-0119391404https://scholars.lib.ntu.edu.tw/handle/123456789/638195Continuous monitoring of oil tanks is vital for analyzing local fuel consumption. Synthetic Aperture Radar (SAR) has been a popular data source as it guarantees day-and-night and all-weather sensing capacity. However, most earlier studies adopt a scene-wise and oil tank-wise scheme, which is inefficient as there can be hundreds of oil tanks on an oil depot, while only a few are dynamic. Also, no study explores both intensity coherence and interferometric coherence for oil tank dynamics mapping. This study proposes a novel three-stage strategy to detect all oil tanks, identify dynamic oil tanks, and estimate their fuel volume changes based on both the intensity and phase information of SAR in both slant-range and geocoded projections. Results indicate that the intensity coherence can perfectly differentiate dynamic and stable oil tanks (a Jeffries–Matusita distance of 1.997) and is less vulnerable to repeat-pass SAR factors, such as baselines and atmospheric conditions. Via evaluating estimations' consistency, our scattering keypoint detection exhibits 0.23 and 0.87 m precision of tank heights and diameters, respectively. By validation with ground truth data, oil tanks exhibiting floating-roof changes larger than 0.23 m are correctly identified. Also, the estimated storage changes agree well with actual changes with an R-squared value of 0.98 and a root-mean-square error (RMSE) corresponding to 1.05 m biases in floating-roof heights. These quantitative assessments confirm the robustness and broad applicability of our non-in situ data-needed approach, highlighting the opportunity to utilize spotlight SAR data to automatically and comprehensively monitor oil tank dynamics in remote sites.Coherence | Fuel storage | Geometry | object detection | Oils | Scattering | scattering feature extraction | spaceborne remote sensing | synthetic aperture radar | Synthetic aperture radar | Training[SDGs]SDG7journal article10.1109/JSTARS.2023.33371262-s2.0-85179051518https://api.elsevier.com/content/abstract/scopus_id/85179051518