Oil Slick Detection Research Articles

Implementation of a semi-automatic approach for detection and extraction of oil slicks using Sentinel-1: Case study of the Moroccan exclusive economic zone

Due to its strategic geographic location, Morocco experiences heavy ship traffic, which inevitably has consequences. Ship accidents and illegal discharges, commonly referred to as oil spills, are frequent and pose significant harm to the marine ecosystem. Therefore, employing new technologies, such as satellite monitoring, is crucial and will greatly enhance the ability to monitor and assess these oil spills. The present work is based on Sentinel-1 SAR images and aims to take into evidence the extent of the hydrocarbon marine pollution in all of the Moroccan EEZ. More than 220 possible cases were detected and mapped during the last decade (2014–2024). A batch process was developed to automate the detection of oil spills by identifying areas with reduced Normalized Radar Cross Section (NRCS) values. This process incorporates corrections, masks, wind field estimation, and oil clustering. The method successfully detected approximately 80% of the cases between Kenitra and Casablanca. The closest detected spill is located 1 km from the coast, the largest spill covers an area of 90 km2, and the most extended spill measures about 145 km. This study can help raise awareness among governmental authorities about potential environmental damage and can be used to train artificial intelligence models, which could simulate oil slick propagation.

A self-evolving deep learning algorithm for automatic oil spill detection in Sentinel-1 SAR images

Oil spill accidents are one of the major problems causing marine pollution, and thus such accidents require rapid detection for early response. In recent years, deep learning algorithms for oil spill detection have been developed for analyzing SAR images. Nevertheless, to generation of deep learning training data using visual inspection is not only a time-consuming and labor-intensive, but also cause bias and lack of diversity in oil slick training data. This has easily led to false positives in challenging SAR images, such as large scale look-alikes, biologic surface films or oil slicks surrounded by look-alikes. In order to accommodate a broader range of SAR oil spill scenes, including those under challenging conditions, it is essential to continuously enhance the performance of oil spill detection algorithms. In this study, a novel self-evolving algorithm for automatic oil spill detection is proposed, which consists of three inter-connected modules: 1) oil spill detection, 2) generation of new training data, and 3) enhancement of deep learning models. The algorithm detected oil slicks automatically while the new SAR image was added, and then the additional high-quality training data was generated by an adaptive thresholding method to increase the performance of deep learning models. In order to detect oil slicks from whole SAR image with oil look-alikes, the variation of the backscattering coefficients near the oil boundary was considered as a key parameter to distinguish oil spills from look-alikes. As a result, after 21 self-evolving training cycles, 27 new training data were generated with an improved F1-score rising from 0.8423 to 0.8896. Despite the existence of look-alikes in various magnitudes, the algorithm successfully identified oil slicks, and the parameters of deep learning models were automatically updated. It was demonstrated that the performance of the algorithm was gradually improved by self-evolving without any human intervention. Additionally, the fundamental limitations of oil spill detection algorithms were mitigated by the proposed approaches, providing more possibilities for subsequent studies.

Detection of marine oil-like features in Sentinel-1 SAR images by supplementary use of deep learning and empirical methods: Performance assessment for the Great Barrier Reef marine park

Continuous monitoring of oil discharges in coastal and open ocean waters using Earth Observation (EO) has undeniably contributed to diminishing their occurrence wherever a detection system was in place, such as in Europe (EMSA's CleanSeaNet) or in the United States (NOAA's OR&R). This study describes the development and testing of a semi-automated oil slick detection system tailored to the Great Barrier Reef (GBR) marine park solely based on EO data as no such service was routinely available in Australia until recently. In this study, a large, curated, historical global dataset of SAR imagery acquired by Sentinel-1 SAR, now publicly available, is used to assess classification techniques, namely an empirical approach and a deep learning model, to discriminate between oil-like features and look-alikes in the scenes acquired over the marine park. An evaluation of this detection system on 10 Sentinel-1 SAR images of the GBR using two performance metrics - the detection accuracy and the false-positive rate (FPR) - shows that the classifiers perform best when combined (accuracy >98 %; FPR 0.01) rather than when used separately. This study demonstrates the benefit of sequentially combining classifiers to improve the detection and monitoring of unreported oil discharge events in SAR imagery. The workflow has also been tested outside the GBR, demonstrating its robustness when applied to other regions such as Australia's Northwest Shelf, Southeast Asia and the Pacific.

Detection of oil slicks in the Persian Gulf using MODIS sensor images

Detection of oil slicks in the Persian Gulf using MODIS sensor images

Integrating Unordered Time Frames in Neural Networks: Application to the Detection of Natural Oil Slicks in Satellite Images

In this paper, we explore the use of novel neural-network architectures to distinguish natural seepages from artificial slicks in Synthetic Aperture Radar (SAR) images. They exploit a distinctive property of natural seepages, which is their temporal recurrence in the same geographical area. This information can be captured in different SAR images acquired at the same location over time, but not necessarily at a regular time-frequency. The proposed neural-network architectures are then built as specific block layers, which efficiently treat the unordered temporal information, followed by more conventional neural-network layers, which are widely used for image classification. Different block layers for unordered temporal information are compared on Sentinel-1 images acquired in the Aegean Sea. Following data augmentation steps, our dataset contains a consistent subset of images gathered among 16000 time-patches. We demonstrate that non-linear time-based block layers and block layers that avoid information bottlenecks are most efficient to discriminate between natural and artificial oil spills. Compared with standard neural-networks which use single time-frames, the integration of unordered temporal information increases the overall accuracy (OA) from 82% to 92% on our dataset, which demonstrates the effectiveness of the proposed approach.

Dark Spot Detection from SAR Images Based on Superpixel Deeper Graph Convolutional Network

Synthetic Aperture Radar (SAR) is the primary equipment used to detect oil slicks on the ocean’s surface. On SAR images, oil spill regions, as well as other places impacted by atmospheric and oceanic phenomena such as rain cells, upwellings, and internal waves, appear as dark spots. Dark spot detection is typically the initial stage in the identification of oil spills. Because the identified dark spots are oil slick candidates, the quality of dark spot segmentation will eventually impact the accuracy of oil slick identification. Although certain sophisticated deep learning approaches employing pixels as primary processing units work well in remote sensing image semantic segmentation, finding some dark patches with weak boundaries and small regions from noisy SAR images remains a significant difficulty. In light of the foregoing, this paper proposes a dark spot detection method based on superpixels and deeper graph convolutional networks (SGDCNs), with superpixels serving as processing units. The contours of dark spots can be better detected after superpixel segmentation, and the noise in the SAR image can also be smoothed. Furthermore, features derived from superpixel regions are more robust than those derived from fixed pixel neighborhoods. Using the support vector machine recursive feature elimination (SVM-RFE) feature selection algorithm, we obtain an excellent subset of superpixel features for segmentation to reduce the learning task difficulty. After that, the SAR images are transformed into graphs with superpixels as nodes, which are fed into the deeper graph convolutional neural network for node classification. SGDCN leverages a differentiable aggregation function to aggregate the node and neighbor features to form more advanced features. To validate our method, we manually annotated six typical large-scale SAR images covering the Baltic Sea and constructed a dark spot detection dataset. The experimental results demonstrate that our proposed SGDCN is robust and effective compared with several competitive baselines. This dataset has been made publicly available along with this paper.

Assessing LAPAN-A3 Satellite with Line Imager Space Application (LISA) Sensor for Oil Spill Detection

LAPAN-A3 (LA3) data has been utilized for earth observation in monitoring natural resources. While most applications are toward land resources monitoring, recent utilization indicates the possibility of LA3 detecting oil spill events on the sea surface. This research provides information regarding the ability of sensors characteristics of LA3 to detect oil slicks and its initial results by examining multispectral bands combination using Optimum Index Factor (OIF), and Digital Number (DN) extraction is carried out on each LA3 band in water-oil-water since LA3 is not able to change DN to reflectance value. In this study, besides using LA3 data, Sentinel-2 data was also used as comparative data and results in validation. Based on the results of the OIF calculation, the combination of the Blue-Green-NIR (BGN) band has the highest value compared to other combinations. This indicates that the BGN band combination is appropriate for visualizing oil and distinguishing between oil and water. The pattern formed from the visualization results with the combination of the BGN band is silvery in crude oil and greenish in ship waste disposal. The result is also strengthened by DN extraction from slick oil samples that shows a prominent pattern on the Blue and Green bands. Finally, this study can conclude that LA3 has great potential to detect oil spills visually but still requires further research for reflectance analysis by converting the DN value into reflectance.

Oil slicks in the Gulf of Guinea – 10 years of Envisat Advanced Synthetic Aperture Radar observations

Abstract. The Gulf of Guinea is a very active area with respect to maritime traffic as well as oil and gas exploitation. Due to the failure of some actors to comply with environmental standards, this region has been subject to a large number of oil pollution episodes. This anthropogenic oil pollution is in addition to natural oil seepage from the ocean floor. This study aims to detect oil slicks in the Gulf of Guinea and to analyse their spatial distribution using synthetic-aperture radar (SAR) images. Previous works have already locally mapped oil slicks in this area; however, to our knowledge, this study is the first to achieve a global statistical analysis based on 10 years of radar images covering 17 exclusive economic zones (EEZs) in the Gulf of Guinea. The present study is based on a database of 3644 SAR images collected between 2002 and 2012 by the Advanced Synthetic Aperture Radar (ASAR) sensor onboard the European Spatial Agency (ESA) Envisat mission, and these images allowed the identification of 18 063 oil slicks. The spatial distribution of these oil slicks is available from Zenodo: https://doi.org/10.5281/zenodo.6470470 (Najoui, 2022b). The oil slicks detected in this work encompass both oil spills of anthropogenic origin and oil seeps of natural origin (natural oil reservoir leaks). A set of 100 georeferenced oil spills is also available from Zenodo: https://doi.org/10.5281/zenodo.6907743 (Najoui, 2022a).

Oil Spill Detection by CP SAR Based on the Power Entropy Decomposition

In recent years, marine oil spills have adversely affected the marine economy and ecosystem, and the detection of marine oil slicks has attracted great attention. Combining different polarimetric features for better oil spill detection is a topic that needs to be studied in depth. Previous studies have shown that the compact polarimetric (CP) synthetic aperture radar (SAR) can be effectively applied to the detection of sea surface oil spill due to its own ability, which is conducive to the extraction of sea surface oil slick. In this paper, we apply the power–entropy (PE) decomposition theory, which decomposes the total scattered power according to the entropy contribution of each cell in the response, to CP SAR data for oil spill detection. The purpose of this study is to enhance the oil slick and the separability of the sea. As a result, an oil spill detection method based on the low-entropy radiation amplitude parameter lesa is proposed. We compare lesa with the other five popular polarimetric features and validate by quantitative evaluation that lesa is superior to other types of polarization feature parameters under different band data. Moreover, the random forest classification is performed on the feature map and achieves the visualization results of oil spill detection. The experimental results show that the lesa can combine the information of the two polarimetric characteristic parameters of entropy and total scattering power, and can clearly indicate the oil slick information under different scenarios.

Detection of Oil Spills in the Northern South China Sea Using Landsat-8 OLI

Petroleum extraction, transportation, and consumption in the marine environment contribute to a large portion of anthropogenic oil spills into the ocean. While previous research focuses more on large oil spill accidents from oil tankers or offshore oil platforms, there are few systematic records on occasional regional oil spills. In this study, optical imagery from Landsat-8 OLI was used to detect oil slicks on the ocean surface through spatial analysis and spectral diagnosis in the northern South China Sea (NSCS). The source of the slicks was identified through datasets from traffic density and platform locations. A total of 632 oil slicks were detected in the NSCS from 2015 to 2019, where 57 were from platforms sources, and 490 were from ships. The average area of the detected slicks was 4.8 km2, and half of the slicks had areas <1.7 km2. Major oil spill hot spots included coastal Guangdong (ship origins), southeast and northeast Dongsha Island (ship origins), middle of south Beibu Gulf (ship and platform origins), and southeast Pearl River Estuary (platform origins). Through this study, we demonstrate the capability of medium-resolution optical imagery in monitoring regional oil spills. Such results and methods may help in near real-time oil spill monitoring and further environmental assessments.

Offshore Oil Slick Detection: From Photo-Interpreter to Explainable Multi-Modal Deep Learning Models Using SAR Images and Contextual Data

Ocean surface monitoring, emphasizing oil slick detection, has become essential due to its importance for oil exploration and ecosystem risk prevention. Automation is now mandatory since the manual annotation process of oil by photo-interpreters is time-consuming and cannot process the data collected continuously by the available spaceborne sensors. Studies on automatic detection methods mainly focus on Synthetic Aperture Radar (SAR) data exclusively to detect anthropogenic (spills) or natural (seeps) oil slicks, all using limited datasets. The main goal is to maximize the detection of oil slicks of both natures while being robust to other phenomena that generate false alarms, called “lookalikes”. To this end, this paper presents the automation of offshore oil slick detection on an extensive database of real and recent oil slick monitoring scenarios, including both types of slicks. It relies on slick annotations performed by expert photo-interpreters on Sentinel-1 SAR data over four years and three areas worldwide. In addition, contextual data such as wind estimates and infrastructure positions are included in the database as they are relevant data for oil detection. The contributions of this paper are: (i) A comparative study of deep learning approaches using SAR data. A semantic and instance segmentation analysis via FC-DenseNet and Mask R-CNN, respectively. (ii) A proposal for Fuse-FC-DenseNet, an extension of FC-DenseNet that fuses heterogeneous SAR and wind speed data for enhanced oil slick segmentation. (iii) An improved set of evaluation metrics dedicated to the task that considers contextual information. (iv) A visual explanation of deep learning predictions based on the SHapley Additive exPlanation (SHAP) method adapted to semantic segmentation. The proposed approach yields a detection performance of up to 94% of good detection with a false alarm reduction ranging from 14% to 34% compared to mono-modal models. These results provide new solutions to improve the detection of natural and anthropogenic oil slicks by providing tools that allow photo-interpreters to work more efficiently on a wide range of marine surfaces to be monitored worldwide. Such a tool will accelerate the oil slick detection task to keep up with the continuous sensor acquisition. This upstream work will allow us to study its possible integration into an industrial production pipeline. In addition, a prediction explanation is proposed, which can be integrated as a step to identify the appropriate methodology for presenting the predictions to the experts and understanding the obtained predictions and their sensitivity to contextual information. Thus it helps them to optimize their way of working.

Oil spill trajectory prediction assessment using multi model ocean currents - case study of MT Dawn Oil spill 2017

Abstract 684276 An assessment was carried out to judge the performance of the modeled ocean currents in oil spill trajectory prediction. Ocean circulation is the key factor in determining the drift pattern of the spilled marine oil pollutant. General National Oceanic and Atmospheric Administration Operational Modeling Environment (GNOME), an oil spill trajectory model, in diagnostic mode was set for simulating drift pattern of Heavy Fuel Oil (HFO). On 28 January 2017, 0345 hrs, Indian Standard Time (IST), approximately 196.4 MT of HFO was spilled due to vessel collision. The oil spill model was set and run during 28-Jan-2017 to 05-Feb-2017 with 196 tons of HFO. Wind velocity fields were obtained from European Centre for Medium-Range Weather Forecasts (ECMWF). The modeled ocean currents were obtained from High resolution Operational Ocean Forecasting and reanalysis System (HOOFS) with two model set ups such as Indian ocean (IO) and Bay Of Bengal (BOB). Ocean current pattern were also obtained from Hybrid Co-ordinate Ocean Model (HYCOM) and Global Ocean Data Assimilation System (GODAS) based Modular Ocean Model (GM4P1). The oil drift patterns were simulated individually for the spillage due to MT Dawn vessel, by forcing GNOME with the above said wind and ocean currents. Radar data obtained for 29-Jan-2017, from Sentinel -1A was processed for detecting oil slicks. The respective drift patterns obtained were compared individually with the oil slick signatures of Sentinel -1A on 29-Jan-2017. It was found that the drift pattern obtained while using the ocean currents of HOOFS_BOB was in better agreement with that of the observed slicks. Unlike other oil drift patterns, offshore spread of the slicks are well captured while using the ocean currents of HOOFS_BOB. This paper illustrates the method of oil spill trajectory prediction using various ocean currents and validating the simulated drift with the ground truth. It also emphasize the need of using various modeled ocean currents in assessing the performance of oil spill trajectory model.

Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data

Oil spills are a potential hazard, causing the deaths of millions of aquatic animals and this leaves a calamitous effect on the marine ecosystem. This research focuses on evaluating the potential of polarimetric parameters in discriminating the oil slick from water and also possible thicker/thinner zones within the slick. For this purpose, L-band UAVSAR quad-pol data of the Gulf of Mexico region is exploited. A total number of 19 polarimetric parameters are examined to study their behavior and ability in distinguishing oil slick from water and its own less or more oil accumulated zones. The simulation of compact-pol data from UAVSAR quad-pol data is carried out which has shown good performance in detection and discrimination of oil slick from water. To know the extent of separation between oil and water classes, a statistical separability analysis is carried out. The outcomes of each polarimetric parameter from separability analysis are then quantified with the radial basis function (RBF) supervised Support Vector Machine classifier followed with an accurate estimation of the results. Moreover, a comparison of the achieved and estimated accuracy has shown a significant drop in accuracy values. It has been observed that the highest accuracy is given by LHV compact-pol decomposition and coherency matrix with a classification accuracy of ~ 94.09% and ~ 94.60%, respectively. The proposed methodology has performed well in discriminating the oil slick by utilizing UAVSAR dataset for both quad-pol and compact-pol simulation.

Marine Oil Slick Detection Using Improved Polarimetric Feature Parameters Based on Polarimetric Synthetic Aperture Radar Data

Marine oil spill detection is vital for strengthening the emergency commands of oil spill accidents and repairing the marine environment after a disaster. Polarimetric Synthetic Aperture Radar (Pol-SAR) can obtain abundant information of the targets by measuring their complex scattering matrices, which is conducive to analyze and interpret the scattering mechanism of oil slicks, look-alikes, and seawater and realize the extraction and detection of oil slicks. The polarimetric features of quad-pol SAR have now been extended to oil spill detection. Inspired by this advancement, we proposed a set of improved polarimetric feature combination based on polarimetric scattering entropy H and the improved anisotropy A12–H_A12. The objective of this study was to improve the distinguishability between oil slicks, look-alikes, and background seawater. First, the oil spill detection capability of the H_A12 combination was observed to be superior than that obtained using the traditional H_A combination; therefore, it can be adopted as an alternate oil spill detection strategy to the latter. Second, H(1 − A12) combination can enhance the scattering randomness of the oil spill target, which outperformed the remaining types of polarimetric feature parameters in different oil spill scenarios, including in respect to the relative thickness information of oil slicks, oil slicks and look-alikes, and different types of oil slicks. The evaluations and comparisons showed that the proposed polarimetric features can indicate the oil slick information and effectively suppress the sea clutter and look-alike information.

Development of the Artificial Intelligence and Optical Sensing Methods for Oil Pollution Monitoring of the Sea by Drones

The oil pollution of seas is increasing, especially in local areas, such as ports, roadsteads of the vessels, and bunkering zones. Today, methods of monitoring seawater are costly and applicable only in the case of big ecology disasters. The development of an operative and reasonable project for monitoring the sea surface for oil slick detection is described in this article using drones equipped with optical sensing and artificial intelligence. The monitoring system is implemented in the form of separate hard and soft frameworks (HSFWs) that combine monitoring methods, hardware, and software. Three frameworks are combined to fulfill the entire monitoring mission. HSFW1 performs the function of autonomous monitoring of thin oil slicks on the sea surface, using computer vision with AI elements for detection, segmentation, and classification of thin slicks. HSFW2 is based on the use of laser-induced fluorescence (LIF) to identify types of oil products that form a slick or that are in a dissolved state, as well as measure their concentration in solution. HSFW3 is designed for autonomous navigation and drone movement control. This article describes AI elements and hardware complexes of the three separate frameworks designed to solve the problems with monitoring slicks of oil products on the sea surface and oil products dissolved in seawater. The results of testing the HSFWs for the detection of pollution caused by marine fuel slicks are described.

Oil seeps detection and mapping by SAR imagery in the Adriatic Sea

This study provides a new perspective on the hydrocarbon seeps in the Adriatic Sea, supporting both the environmental issues and eventual oil and gas exploration. Remote sensing techniques, particularly synthetic aperture radar (SAR) images are used to detect oil slicks on the sea surface. In such a way two groups of oil slicks have been detected in the Northern and Central Adriatic Sea on the SAR images acquired by the European Sentinel-1A and Sentinel-1B satellites in 2017-2018. Analysis of SAR images together with bathymetry, geological and geophysical data in geographic information system (GIS) have shown that these oil slicks, visible on the sea surface have natural origin, i.e. are seepage phenomena, associated to existing marine hydrocarbon systems. These oil slicks were concentrated over the shallow shelf, and repeatedly or episodically occur in the areas of interest, and are related to local oil-and-gas bearing sediments. Findings of the seeps and seep candidates that passed unnoticed from public attention indicate the effectiveness of the approach used. Finally, it is concluded that the frequent SAR imagery is an excellent tool for monitoring of seepage phenomena, and the results indicate that the use of remote sensing methods can be considered as a good approach to support oil and gas exploration in the Adriatic Basin.

Detection of Oil Slicks in the Adriatic Sea (Mediterranean) Using Satellite SAR Imagery

Detection of Oil Slicks in the Adriatic Sea (Mediterranean) Using Satellite SAR Imagery

Detecting Oil Slicks Under the Heterogeneous Marine Environment Utilizing Multispectral Images

Oil spill incidents endanger the marine environment. A spaceborne multispectral imager can be used to observe the floating oil slicks on a large scale. However, traditional methods are only suitable for small-scale detection situations. It is difficult to distinguish oil slicks from the heterogeneous marine environment in large area images because the spectral information on oil slicks and seawater are analogous and mutative. We suppose that finding out the sensitivities of spectral indices to seawater environment contributes to avoiding identifying seawater as oil slick look-alikes. Based on this assumption, this research conducted experiments to test several proposed oil slick detection indices: fluorescence index (FI), rotation-absorption index (RAI), shortwave infrared (SWIR) reflectance, chlorophyll (CHL) spectral characteristic, and colored dissolved organic matter (CDOM) spectral characteristic. The experimental results indicated that the indices were sensitive to different marine environments and combining the complementary indices could extract oil slicks from the heterogeneous marine environment accurately.

Synthetic Aperture Radar Remote Sensing of Operational Platform Produced Water Releases

Oil spill detection services based on satellite synthetic aperture radar (SAR) frequently detect oil slicks close to platforms due to legal releases of produced water. Separating these slicks from larger releases, e.g., due to accidental leakage is challenging. The aim of this work is to investigate the SAR characteristics of produced water, including the typical appearance in HH/VV data, possible variations with oil volume, and limitations on detectability. The study is based on dual-polarization TerraSAR-X data collected with constant imaging geometry over one platform in the North Sea. Despite the low oil content (volume percentage of 0.001%–0.002% in this data set), produced water is clearly detectable, with median damping ratios around 3–9 dB. Produced water is detected here in wind speeds of 2–12 m/s, with reduced detectability above ca 9 m/s. Hourly average release volumes with an oil component as low as 0.003 m 3 are detected. The damping ratio, polarization difference, and co-polarization power ratio are investigated and show no clear correlation with released oil volume. However, some indications of trends such as increasing signal damping with oil volume should be further investigated when data over larger release volumes are available. When comparing the properties of the entire slick with the most recently released part, similar or slightly higher damping ratios were found in the full slick case.

Marine Oil Slick Detection Based on Multi-Polarimetric Features Matching Method Using Polarimetric Synthetic Aperture Radar Data.

Polarimetric synthetic aperture radar is an important tool in the effective detection of marine oil spills. In this study, two cases of Radarsat-2 Fine mode quad-polarimetric synthetic aperture radar datasets are exploited to detect a well-known oil seep area that collected over the Gulf of Mexico using the same research area, sensor, and time. A novel oil spill detection scheme based on a multi-polarimetric features model matching method using spectral pan-similarity measure (SPM) is proposed. A multi-polarimetric features curve is generated based on optimal polarimetric features selected using Jeffreys–Matusita distance considering its ability to discriminate between thick and thin oil slicks and seawater. The SPM is used to search for and match homogeneous unlabeled pixels and assign them to a class with the highest similarity to their spectral vector size, spectral curve shape, and spectral information content. The superiority of the SPM for oil spill detection compared to traditional spectral similarity measures is demonstrated for the first time based on accuracy assessments and computational complexity analysis by comparing with four traditional spectral similarity measures, random forest (RF), support vector machine (SVM), and decision tree (DT). Experiment results indicate that the proposed method has better oil spill detection capability, with a higher average accuracy and kappa coefficient (1.5–7.9% and 1–25% higher, respectively) than the four traditional spectral similarity measures under the same computational complexity operations. Furthermore, in most cases, the proposed method produces valuable and acceptable results that are better than the RF, SVM, and DT in terms of accuracy and computational complexity.