Spaceborne Synthetic Aperture Radar Imagery Research Articles

Ocean eddy detection based on YOLO deep learning algorithm by synthetic aperture radar data

Ocean eddies play a crucial role in the global energy cycle and significantly impact the transport of heat, salt, and nutrients in the global ocean. Spaceborne synthetic aperture radar (SAR), with its high spatial resolution (O(20 m)) and wide coverage, is an important tool for studying ocean eddies. In this paper, we propose a deep-learning network, named EOLO, to automatically detect ocean Eddies observed in C-band spaceborne SAR imagery, based on the You-Only-Look-Once (YOLO) deep learning algorithm. A Sentinel-1 (S1) SAR data-based ocean eddy dataset (named EddyDataset) is established to train the EOLO network. To effectively improve the performance of the EOLO network, we introduced a channel attention mechanism, adopted the up-sampling operator with the larger receptive field, and improved feature fusion method, anchor box size, and loss function. With the support of a high-performance detection network, the geographic information extraction module based on the affine geographic transformation model and a data preprocessing module were added, making EOLO an application-level framework. Our experiment results on EddyDataset demonstrate that EOLO achieves a high quality of eddy detection with 91.5% average precision. We further applied EOLO to entire scenes of S1 images to detect eddies in the Red Sea, the Baltic Sea, and the Western Mediterranean Sea, achieving 96.6%, 98.8% and 98.9% precision, respectively. Moreover, EOLO was used to extract size and location information of ocean eddies based on 6135 S1 scenes acquired in 2021 over the Western Mediterranean Sea. Their spatial characteristics were derived and compared with the eddies extracted from radar altimeter data, presenting interesting discrepancies between sub-mesoscale and mesoscale eddies in the ocean.

Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval

Spaceborne synthetic aperture radar (SAR) can provide accurate images of the ocean surface roughness day-or-night in nearly all weather conditions, being an unique asset for many geophysical applications. Considering the huge amount of data daily acquired by satellites, automated techniques for physical features extraction are needed. Even if supervised deep learning methods attain state-of-the-art results, they require a great amount of labelled data, which are difficult and excessively expensive to acquire for ocean SAR imagery. To this end, we use the subaperture decomposition (SD) algorithm to enhance the unsupervised learning retrieval on the ocean surface, empowering ocean researchers to search into large ocean databases. We empirically prove that SD improves the retrieval precision with over 20% for an unsupervised transformer auto-encoder network. Moreover, we show that SD brings an important performance boost when Doppler centroid images are used as input data, leading the way to new unsupervised physics guided retrieval algorithms.

Dynamic detection of offshore wind turbines by spatial machine learning from spaceborne synthetic aperture radar imagery

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.

Tropical Cyclone Winds and Inflow Angle Asymmetry From SAR Imagery

AbstractThis study developed a morphological model for Tropical Cyclone (TC) wind and inflow angle asymmetry based on sea surface wind fields derived from spaceborne synthetic aperture radar (SAR) images. The model extracts the standard TC morphological information (center, intensity, and radius of the maximum wind) and decomposes the SAR‐derived winds into vortex rotation winds and motion vector, making the reconstruction of the entire TC structure reliable, even in areas not mapped by SAR. The derived wind speeds and inflow angles are verified with aircraft measurements by stepped‐frequency microwave radiometer and dropsondes, obtaining root‐mean‐square errors of 4.32 m/s and 16.04°, respectively. A systematic analysis of 130 SAR TCs images, collected by RADARSAT‐2 and SENTINEL‐1, reveals that the model can capture the main asymmetrical TC structure. Both TC asymmetry and eye size decrease as TC intensity increases.

Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning

Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km2. When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km2 and 1,397.50 km2, respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally.

Layover Compensation Method for Regional Spaceborne SAR Imagery Without GCPs

Synthetic aperture radar (SAR) images reveal severe geometric distortions especially in the mountain area, such as layover, which is caused by imaging characteristics of SAR itself and terrain undulations. The layover phenomenon greatly limits the application of SAR images. This article proposes a layover compensation method for regional spaceborne SAR imagery without ground control points (GCPs), which is mainly improved from two aspects. First, a method based on rational function model (RFM) to determine the layover range is proposed. Second, based on geometric calibration and block adjustment, the processing flow is optimized to generate digital orthophoto map (DOM) which greatly eliminated the influence of layover. The proposed method was applied to Chinese Gaofen-3 (GF-3) SAR regional images, including the ascending and descending track stacks. The result showed that 84.5% of the layover pixels on the regional DOM were compensated, which verified the effectiveness and feasibility of the method.

Classification-Aided SAR and AIS Data Fusion for Space-Based Maritime Surveillance

A wide range of research activities exploit spaceborne Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) for applications that contribute to maritime safety and security. An important requirement of SAR and AIS data fusion is accurate data association (or correlation), which is the process of linking SAR ship detections and AIS observations considered to be of a common origin. The data association is particularly difficult in dense shipping environments, where ships detected in SAR imagery can be wrongly associated with AIS observations. This often results in an erroneous and/or inaccurate maritime picture. Therefore, a classification-aided data association technique is proposed which uses a transfer learning method to classify ship types in SAR imagery. Specifically, a ship classification model is first trained on AIS data and then transferred to make predictions on SAR ship detections. These predictions are subsequently used in the data association which uses a rank-ordered assignment technique to provide a robust match between the data. Two case studies in the UK are used to evaluate the performance of the classification-aided data association technique based on the types of SAR product used for maritime surveillance: wide-area and large-scale data association in the English Channel and focused data association in the Solent. Results show a high level of correspondence between the data that is robust to dense shipping or high traffic, and the confidence in the data association is improved when using class (i.e., ship type) information.

On the Use of Multipolarization Satellite SAR Data for Coastline Extraction in Harsh Coastal Environments: The Case of Solway Firth

This study deals with coastline extraction using multipolarization spaceborne synthetic aperture radar (SAR) imagery acquired over coastal intertidal areas. The latter are very challenging environments where mud flats lead to a large variability of normalized radar cross section, which may trigger a significant number of false edges during the extraction process. The performance of SAR-based coastline extraction methods that rely on a joint combination of multipolarization information (either single- or dual-polarization metrics) and speckle filtering (either local and nonlocal approaches) are analyzed using global positioning system (GPS) samples and colocated SAR imagery collected under different incidence angles. Our test site is an intertidal zone with a wetland (i.e., salt marsh) in the Solway Firth, south-west along the Scottish-English border. Experimental results, obtained processing a pair of RadarSAT-2 full-polarimetric and a pair of Sentinel-1 dual-polarimetric SAR imagery augmented by colocated GPS samples, show that: first, the multipolarization information outperforms the single-polarization counterpart in terms of extraction accuracy; second, among the single-polarization channels, the cross-polarized one performs best; third, both single- and dual-polarization methods perform better when nonlocal speckle filtering is applied; fourth, the joint combination of nonlocal speckle filter and dual-polarization information provides the best accuracy; and finally, the incidence angle plays a role in the extraction accuracy with larger incidence angles resulting in the best performance when dual-polarization metric is used.

Estimating tree age of rubber stands using spaceborne L-band synthetic aperture radar

Earth-observing data have been employed for plantation’s detection and monitoring within the context of land cover planning. Rising utilization has recently been observed for detailed tree assessment on very specific commodities such as oil palm. In this article, evaluation of spaceborne Synthetic Aperture Radar (SAR) is presented, taking the outstanding benefit of high resolution fully polarimetric data recently available worldwide. Primary goal of the research was to evaluate SAR imagery for the detection of stand age of rubber plantation. We found that coupling fully polarimetric data and Random Forests (RF) as the classifier could yield more than 80% accuracy. This indicates that Earth-observing data serve as potential backbone for mapping and investigating plantations in remote locations.

Geolocation Accuracy Improvement of Multiobserved GF-3 Spaceborne SAR Imagery

As the first C-band full-polarization spaceborne synthetic aperture radar (SAR) satellite in China, Gaofen-3 (GF-3) is of great significance in scientific research and economic development. Thanks to the advantage of all-weather and all-day observation, spaceborne SAR imagery is widely used in digital elevation model (DEM) generation, target tracking, and so on. And, all these applications are based on the high 3-D geolocation accuracy of SAR imagery. Therefore, a new slant range error update model and an error-source-based weight strategy which can effectively improve the 3-D geolocation accuracy of multiobserved data set from GF-3 satellite are proposed in this letter. The slant range error update model is proposed to improve the 3-D geolocation accuracy using the virtual control information in height direction. At the same time, an error-source-based weight strategy is introduced as an appendant to the multiobservation block adjustment model based on the rational function model (RFM). The experimental results demonstrate that our proposed method can achieve a much higher 3-D geolocation accuracy than the traditional methods.

Ship Velocity Estimation From Ship Wakes Detected Using Convolutional Neural Networks

Accurately tracking marine traffic considering security and commercial activities is still challenging despite its increasing global importance. Recently, space-borne synthetic aperture radar (SAR) is being considered to accurately monitor maritime traffic, and techniques to detect the position of ships and estimate their velocity have become essential. Here, we investigated the potential for automatic estimation of ship velocity using the azimuth offset between ships and wakes detected using convolutional neural network (CNN) coupled with SAR imagery. We found that azimuth offset is proportional to the Doppler shift effect of the back-scattered signal in SAR, thus, it relates to the radial velocity of a moving target. Consequently, we propose a method whereby a CNN is applied to automatically detect ship wakes from TanDEM-X data. In this method, ship velocity is calculated using the azimuthal distance (i.e., azimuth offset) between the stern of the detected ship and the vertex of the detected V-shape wake—determined as the intersection of two lines obtained through edge filtering and Radon transforms. The location and number of detected ships are then compared with an automatic identification system (AIS), and the calculated velocity of the ship is compared with the velocity obtained via along-track interferometry and AIS. Results show that our method automatically detects ships and wakes with accuracies of 91.0% and 93.2%, respectively, and estimates the velocity of ships with an accuracy of 0.13 m/s. This method is effective when wind velocities are not substantially higher than 5.5 m/s and ship velocities are not extremely low.

Geometric Self-Calibration of YaoGan-13 Images Using Multiple Overlapping Images.

Geometric calibration is an important means of improving the absolute positioning accuracy of space-borne synthetic aperture radar imagery. The conventional calibration method is based on a calibration field, which is simple and convenient, but requires a great deal of manpower and material resources to obtain ground control points. Although newer cross-calibration methods do not require ground control points, calibration accuracy still depends on a periodically updated reference image. Accordingly, this study proposes a geometric self-calibration method based on the positioning consistency constraint of conjugate image points to provide rapid and accurate calibration of the YaoGan-13 satellite. The proposed method can accurately calibrate geometric parameters without requiring ground control points or high-precision reference images. To verify the absolute positioning accuracy obtained using the proposed self-calibration method, YaoGan-13 Stripmap images of multiple regions were collected and evaluated. The results indicate that high-accuracy absolute positioning can be achieved with a plane accuracy of 3.83 m or better for Stripmap data, without regarding elevation error. Compared to the conventional calibration method using high-accuracy control data, the difference between the two methods is only about 2.53 m, less than the 3-m resolution of the image, verifying the effectiveness of the proposed self-calibration method.

A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery

The ghost phenomenon in synthetic aperture radar (SAR) imaging is primarily caused by azimuth or range ambiguities, which cause difficulties in SAR target detection application. To mitigate this influence, we propose a ship target detection method in spaceborne SAR imagery, using a hierarchical convolutional neural network (H-CNN). Based on the nature of ghost replicas and typical target classes, a two-stage CNN model is built to detect ship targets against sea clutter and the ghost. First, regions of interest (ROIs) were extracted from a large imaged scene during the coarse-detection stage. Unwanted ghost replicas represented major residual interference sources in ROIs, therefore, the other CNN process was executed during the fine-detection stage. Finally, comparative experiments and analyses, using Sentinel-1 SAR data and various assessment criteria, were conducted to validate H-CNN. Our results showed that the proposed method can outperform the conventional constant false-alarm rate technique and CNN-based models.

Measuring Coseismic Deformation With Spaceborne Synthetic Aperture Radar: A Review

In the past 25 years, space-borne Synthetic Aperture Radar imagery has become an increasingly available data source for the study of crustal deformation associated with moderate to large earthquakes (M>4.0). Coseismic surface deformation can be measured with several well-established techniques, the applicability of which depends on the ground displacement pattern, on several radar parameters, and on the surface properties at the time of the radar acquisitions. The state-of-the-art concerning the measurement techniques is reviewed, and their application to over 100 case-studies since the launch of the Sentinel-1a satellite is discussed, including the performance of the different methods and the data processing aspects, which still constitute topics of ongoing research.

Tandem-L: A Technical Perspective on Future Spaceborne SAR Sensors for Earth Observation

Tandem-L is proposed as a spaceborne synthetic aperture radar (SAR) mission developed and operated by the German Aerospace Center in cooperation with several Helmholtz research centers and the German space industries. The mission concept comprises two fully polarimetric radar satellites providing monostatic and bistatic SAR imagery. A key feature of these SAR sensors is the employment of large lightweight unfurlable mesh reflectors fed by digital feed arrays. The main advantage of this new SAR system concept is the provision of large antenna apertures in space and flexible operation via reconfigurable feed electronics. By this, it becomes possible to map, for the first time, a continuous 350-km wide swath with a 7-m azimuth resolution with excellent noise equivalent sigma zero and ambiguity suppression. This paper shall give an overview on the technical aspects of the Tandem-L SAR instrument and antenna design. In particular, after a short review of the SAR system requirements, the concept of reflector SAR systems is outlined and the operation principle is presented. General guidelines for the design of array-fed reflector antennas with application to SAR imaging are given. Then, the optimization approach of the feed array design is detailed with a specific emphasis on a fixed beamforming concept in azimuth. In this context, also the problem of cross-pol pattern mitigation is addressed. These optimization steps are shown to be crucial for achieving the performance requirements in quad-pol acquisitions. Beamforming in elevation is performed onboard the spacecraft via digital hardware. This paper presents the beamforming architecture on receive for Tandem-L, which would apply in general for instance also to planar multielevation beam SAR antennas with Scan-On-Receive capabilities. Tandem-L is operated as a staggered SAR, which means varying the pulse repetition interval from pulse to pulse. In this context, the major design challenges are presented. Moreover, the impact of pulse staggering on the imaging performance is discussed. Tandem-L’s SAR performance is presented by means of numerical simulations showing that the performance requirements imposed by the scientific user community could be met. The final part of this paper addresses options for high azimuth resolution imaging as well as a beamforming method for enhanced range ambiguity suppression.

Simultaneous range ambiguity mitigation and sidelobe reduction using orthogonal non-linear frequency modulated (ONLFM) signals for satellite SAR Imaging

ABSTRACTThis letter first attempts to simultaneously reduce range ambiguities and sidelobes in spaceborne synthetic aperture radar (SAR). Two of the limitations of SAR are the range ambiguity phenomenon and the sidelobes effect. The range ambiguity appears with long delayed echoes. The current transmitted signal does not contribute to the reflected echo that is received after the transmission of one or more new pulses. Moreover, the conventional radar commonly uses linear frequency modulated (LFM) waveforms, producing a peak sidelobe ratio (PSLR) of −13.25 dB. Unfortunately, the sidelobes in SAR imagery contribute to speckle and interfere with nearby scatterers. This letter investigates the opportunity of designing the transmitted signals using orthogonal non-linear frequency modulated (ONLFM) waveform. The results demonstrate that ONLFM signals outperform conventional LFM signal. Using the proposed ONLFM waveforms, the PSLR is reduced to −50.25 dB, meanwhile the range ambiguities can be greatly suppressed, despite broadening the mainlobe.

Analyzing floating and bedfast lake ice regimes across Arctic Alaska using 25 years of space-borne SAR imagery

Late-winter lake ice regimes are controlled by water depth relative to maximum ice thickness (MIT). When MIT exceeds maximum water depth, lakes freeze to the bottom with bedfast ice (BI) and when MIT is less than maximum water depth lakes have floating ice (FI). Both airborne radar and space-borne synthetic aperture radar (SAR) imagery (Ku-, X-, C-, and L-band) have been used previously to determine whether lakes have a BI or FI regime in a given year, across a number of years, or across large regions. In this study, we use a combination of ERS-1/2, RADARSAT-2, Envisat, and Sentinel-1 SAR imagery for seven lake-rich regions in Arctic Alaska to analyze lake ice regime extents and dynamics over a 25-year period (1992–2016). Our interactive threshold classification method determines a unique statistic-based intensity threshold for each SAR scene, allowing for the comparison of classification results from C-band SAR data acquired with different polarizations and incidence angles. Additionally, our novel method accommodates declining signal strength in aging extended-mission satellite SAR instruments. Comparison of SAR ice regime classifications with extensive field measurements from six years yielded a 93% accuracy. Significant declines in BI regimes were only observed in the Fish Creek area with 3% of lakes exhibiting transitional ice regimes—lakes that switch from BI to FI during this 25-year period. This analysis suggests that the potential conversion from BI to FI regimes is primarily a function of lake depth distributions in addition to regional differences in climate variability. Remote sensing of lake ice regimes with C-band SAR is a useful tool to monitor the associated thermal impacts on permafrost, since lake ice regimes can be used as a proxy for of sub-lake permafrost thaw, considered by the Global Climate Observing System as an Essential Climate Variable (ECV). Continued winter warming and variable snow conditions in the Arctic are expected and our long-term analysis provides a valuable baseline for predicting where potential future lake ice regimes shifts will be most pronounced.

An Image-Segmentation-Based Framework to Detect Oil Slicks From Moving Vessels in the Southern African Oceans Using SAR Imagery

Oil slick events caused due to bilge leakage/dumps from ships and from other anthropogenic sources pose a threat to the aquatic ecosystem and need to be monitored on a regular basis. An automatic image-segmentation-based framework to detect oil slick from moving vessels using spaceborne synthetic aperture radar (SAR) images over Southern African oceans was proposed. The study uses an automated threshold-based algorithm and a region-based algorithm to achieve a more efficient oil slick detection. The proposed framework consisted of two parts: First, a threshold-based method was used to detect areas with a high oil slick probability; second, a region-based method was used to extract the full extent of the detected oil slick. The proposed framework was tested on both real SAR and synthetic SAR images and was robust to intensity variations, weak boundaries, and was also more computationally efficient when compared to the region-based method without the threshold-based input.

Multidimensional Small Baseline Subset (MSBAS) for volcano monitoring in two dimensions: Opportunities and challenges. Case study Piton de la Fournaise volcano

Space-borne Synthetic Aperture Radar (SAR) provides an opportunity for monitoring ground deformation at active volcanoes with high temporal and spatial resolutions. Modern SAR satellites acquire very large volumes of data that no longer can be effectively and efficiently processed and interpreted manually. The development of novel automatic processing methodologies is warranted in order to fully utilize big data. The Multidimensional Small Baseline Subset (MSBAS) methodology is an example of the semi-automatic processing system for computing temporally dense two-dimensional, horizontal east-west and vertical time series of ground deformation from ascending and descending SAR imagery acquired by various satellites. Here MSBAS is used for mapping ground deformation at the Piton de la Fournaise volcano (La Réunion Island, France) during the February 2012–April 2016 period from RADARSAT-2 data. Five volcanic eruptions occurred during the June 2014–October 2015 period, producing over 60cm of horizontal and over 30cm of vertical ground deformation, well-resolved in the MSBAS-derived time series. Validation of DInSAR results by comparison with GNSS observations and modeling of the two last and largest eruptions was performed. Validation showed good overall agreement between DInSAR and GNSS observations while revealing the benefits and limitations of both techniques. Modeling of fault and dike geometries attempted to explain the dis-proportionally large eastward motion of the eastern flank of the Piton de la Fournaise volcano that occurred during these eruptions. We demonstrated that the simple elastic model consisting of two dikes and a sliding surface can account for the observed ground deformation.

Dark-spot segmentation for oil spill detection based on multifeature fusion classification in single-pol synthetic aperture radar imagery

In recent years, oil spill surveillance with space-borne synthetic aperture radar (SAR) has received unprecedented attention and has been gradually developed into a common technique for maritime environment protection. A typical SAR-based oil spill detection process consists of three steps: (1) dark-spot segmentation, (2) feature extraction, and (3) oil spill and look-alike discrimination. As a preliminary task in the oil spill detection process chain, dark-spot segmentation is a critical and fundamental step prior to feature extraction and classification, since its output has a direct impact on the two subsequent stages. The balance between the detection probability and false alarm probability has a vital impact on the performance of the entire detection system. Unfortunately, this problem has not drawn as much attention as the other two stages. A specific effort has been placed on dark-spot segmentation in single-pol SAR imagery. A combination of fine designed features, including gray features, geometric features, and textural features, is proposed to characterize the oil spill and seawater for improving the performance of dark-spot segmentation. In the proposed process chain, a histogram stretching transform is incorporated before the gray feature extraction to enhance the contrast between possible oil spills and water. A simple but effective multiple-level thresholding algorithm is developed to conduct a binary classification before the geometric feature extraction to obtain more accurate area features. A local binary pattern code is computed and assigned as the textural feature for a pixel to characterize the physical difference between oil spills and water. The experimental result confirms that the proposed fine designed feature combination outperforms existing approaches in both aspects of overall segmentation accuracy and the capability to balance detection probability and false alarm probability. It is a promising alternative that can be incorporated into existing oil spill detection systems to further improve system performance.