**2.6 Comparison to latest state-of-the-art techniques<sup>1</sup>**

Several state-of-the-art techniques have been already included when we introduced the sensors (Section 2.1) and platforms (Section 2.2). To follow up on Section 2.5 where we describe the importance of the proposed approach—and for convenience

<sup>1</sup> It is important to keep in mind that in this section we are not including all state-of-the-art techniques used for oil spill monitoring irrespective of the sensors and platforms used.

### *Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing DOI: http://dx.doi.org/10.5772/intechopen.106942*

—we only focus in this section on the recently developed techniques that target any of the three functionalities during oil spill monitoring (detection, estimation, and classification) using non-satellite-based approaches.

Several drone-based techniques are recently suggested for oil spill monitoring. Saleem et al. [53] presents thermal imaging methods and tools applied to simulated and lab-based experimental data to detect oil spills in seawater using drones. The simulation environment is made to be very similar to the Gulf of Mexico spill in 2010. The drone can be used in two operational modes: the first mode is when the spill incident is known, then the task is to locate it exactly. The second mode is when operating to look for potential spills and for nearby ships that caused them. After collecting thermal images using the thermal camera mounted on an unmanned aerial vehicle (UAV), the temperature profile of collected data is studied using image processing techniques. The proposed system shows the ability to draw a rectangular contour around the spill region but not the accurate contour. The thickness of the spill under test is not reported. Another drone-based solution equipped with a thermal camera is proposed in [54]. The system is suggested for industries, especially for the Bahrain Petroleum Company (Bapco), to inspect oil and gas leakages. It uses AI-based (decision tree (DT), random forest (RF), and support vector machine (SVM)) onboard processing to monitor oil pipelines for possible leakages and cracks. The training is done based on a real dataset of methane pipeline leakage detection provided by Bapco, Tatweer Petroleum, and the National Space Science Authority (NSSA). The proposed algorithms are accelerated on hardware using parallel processing to allow real-time alerting with less than 100 milliseconds delay. No results for oil spill leakage from pipelines are presented. The presented work is relying only on a single dataset while multiple datasets should be included. Oliveira et al. [55] also targets the first functionality of the monitoring system (detection) by presenting a UAV-based aerial solution to automatically identify the contour and localize oil spills. This approach is tested on simulated, experimental, and field data through simulated oil spills in the Leixoes Harbour and the Douro river in Porto in Portugal and in the Puerto de A Coruna in Spain. In [56], a deep-learning-based method is proposed to control marine oil pollution. It implements an SVM approach that processes visual camera images to identify oil-polluted areas. Afterward, it predicts the movement of the polluted area by calculating the optical flows. The algorithm is applied to the 2016 Tegent Cruises dataset in Shimen in northern Taiwan. The accuracy of the SVM classification varies between 85.71 and 99.95%. Another deep learning approach is suggested by [57]. It presents a CNN-based novel framework to detect small oil spills inside a port using a thermal infrared camera mounted on a drone. Three kinds of oil (HFO, DMA, and ULSFO) that are frequently used in the port of Antwerp are included during the experiment. A mean intersection over union (mIoU) of 89% is achieved. The proposed technique is functional within a 31.9 m by 42.1 m field of view and is useful for relatively small oil spills. Jiang et al. [58] uses hyperspectral images to feed a onedimensional convolutional neural network to identify the type of oil spills. Using the adaptive long-term moment estimation (ALTME) optimizer, the oil spill spectral information is learned. The experiment is conducted 20 m away from the Yellow Sea Shore in China, in the pool of Qingdao Scientific Research Base. The technique achieves a detection accuracy of more than 98.09% in detecting different predefined classes of oil films of thicknesses between 1 and 3.5 mm. To summarize, [53–57] all target spill detection (functionality #1), whereas [58] also targets the thickness estimation (functionality #2) in predefined classes (between 1 and 3.5 mm) and the oil type identification (functionality #3). This reinforces the need for our new proposed

system that targets the three different functionalities at the same time. Compared with the work presented in [58], the proposed new solution is using the wide-band radar sensor instead of a hyperspectral visual sensor (check differences in Section 2.1). Furthermore, the new approach uses maximum-likelihood algorithms on top of different machine learning algorithms to provide the results for monitoring. Also, the approach aims the thickness estimation up to 10 mm value, and it is feasible to be implemented on drones for onboard processing.

Other techniques, which are not drone-based, are also proposed in the literature for oil spill monitoring. Yin et al. [59] proposes an optical fiber surface plasmon resonance for oil spill detection and thickness estimation. At the water-oil and ail-oil interfaces, the sensor records an absolute sensitivity of 1.373%/mm and 2.742%/mm in the thickness ranges 0–5 mm and 0–10 mm, respectively. Although their approach can detect thicknesses in the 1–10 mm range, however, it requires in-situ sampling. Li et al. [60] presents multiple machine learning algorithms (RF, SVM, DNN, and DNN with differential pooling) that process images from the high-resolution hyperspectral sensor to identify the oil type based on the reflectance spectra. Four types of oil are tested in this work, including crude oil, diesel, lubricant, and heavy diesel. The oil thickness and the wind conditions are not provided as inputs to the machine learning models to test their accuracy with the minimal amount of available information. All tested models can differentiate between heavy oils. But for light oils, RF fails to do the correct classification, while the neural network models provide better classification than the SVM. This technique is not remote-based and should be used after the detection of oil spills. Dala et al. [61] develops a novel microwave oil spill sensor to determine the thickness in the range of 10s mm using an ultra-wideband radar operating between 0.3 and 3000 MHz. The performance of the system is tested under static experimental conditions. When deploying the system in the sea, the impact of the waves should be considered.

A comparison between the functionalities provided by the developed techniques above and our new approach is presented in **Table 1**.
