https://scholars.lib.ntu.edu.tw/handle/123456789/638195
標題: | Automatic Monitoring of Oil Tank 3D Geometry and Storage Changes with Interferometric Coherence and SAR Intensity Information | 作者: | YA-LUN TSAI Huang, Chun Jia Chen, Chia Ling JEN-YU HAN |
關鍵字: | Coherence | Fuel storage | Geometry | object detection | Oils | Scattering | scattering feature extraction | spaceborne remote sensing | synthetic aperture radar | Synthetic aperture radar | Training | 公開日期: | 1-一月-2023 | 卷: | 17 | 來源出版物: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | 摘要: | Continuous 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/638195 | ISSN: | 19391404 | DOI: | 10.1109/JSTARS.2023.3337126 |
顯示於: | 土木工程學系 |
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