Journal of King Saud University: Computer and Information Sciences (May 2022)
Dynamic detection of offshore wind turbines by spatial machine learning from spaceborne synthetic aperture radar imagery
Abstract
In order to change the energy supply structure of traditional thermal power generation with excessive carbon emissions, more and more offshore wind turbines are being installed and put into operation worldwide. However, the dynamic monitoring of offshore wind turbines is still an emerging problem to be solved at a global scale. This paper proposes an effective approach for dynamic detection of offshore wind turbines by machine learning from spaceborne synthetic aperture radar (SAR) Sentinel-1 satellite data. First, the radar images are preprocessed to reduce the speckle noise and ocean wave clutter. Specifically, the time series radar data are cumulatively averaged according to the monthly interval to eliminate the influence of moving temporary objects such as ships; then, the speckle noise is suppressed using refined lee filter; the imagery affected by ocean wave clutters are also mitigated by using a constant false alarm rate (CFAR) technique. Next, representative data are carefully selected as the labeled training data set; and the random forest (RF) model is trained on the Google Earth Engine (GEE) cloud computing platform. Validation demonstrates an overall accuracy of 99.99%, a producer accuracy of 100%, and an user accuracy of 94.12%. Then, mathematical morphology-based time series spatial data differentiation is proposed for monitoring the change of wind turbine. Finally, the dynamic change of the offshore wind farms in the Yellow Sea of China and the North Sea of Europe are monitored by using the proposed approach from 2015 to 2021. Cross-comparisons with ground truth data show an accuracy of 93.67% for dynamic detection of offshore wind turbines. From a large number of experimental results, it is shown that the proposed approach has the advantages of large scale monitoring and high precision, and can be used for global dynamic detection of offshore wind turbines.
