Synthetic Aperture Radar Oil Spill Research Articles

Marine Oil Spill Detection from Low-Quality SAR Remote Sensing Images

Oil spills pose a significant threat to the marine ecological environment. The intelligent interpretation of synthetic aperture radar (SAR) remote sensing images serves as a crucial approach to marine oil spill detection, offering the potential for real-time, continuous, and accurate monitoring. This study makes valuable contributions to the field of marine oil spill detection based on low-quality SAR images, focusing on the following key aspects: (1) We thoroughly analyze the Deep SAR Oil Spill dataset, known as the SOS dataset, a prominent resource in the domain of marine oil spill detection from low-quality SAR images, and rectify identified issues to ensure its reliability. (2) By identifying and rectifying errors in the original literature that presented the SOS dataset, and reproducing the experiments to provide accurate results, benchmark performance metrics for marine oil spill detection with low-quality SAR remote sensing images are established. (3) We propose three progressive deep learning-based marine oil spill detection methods (a direct detection method based on Transformer and UNet, a detection method based on FFDNet and TransUNet with denoising before detection, and a detection method based on integrated multi-model learning) and the performance advantages of the proposed methods are verified by comparing them with semantic segmentation models such as UNet, SegNet, and DeepLabV3+. (4) We introduce a feasible, highly robust and easily scalable system architecture approach that effectively addresses practical engineering applications. This paper is an important addition to the research on marine oil spill detection from low-quality SAR images, and the proposed experimental method and performance details can provide a reference for related research.

A New Technique for Segmentation of the Oil Spills From Synthetic-Aperture Radar Images Using Convolutional Neural Network

Oil spills have proven to have detrimental effects on the marine-based environment and economy. Thus, it is necessary to identify oil spills and classify them in the sea to reduce oil-induced pollution in seas and oceans. Synthetic-aperture radar (SAR) imaging is a good option for rapid oil detection, as it covers a wide area, collects data at short intervals, and allows taking images in all weather conditions throughout the day. The reason for using deep neural networks is that training several images enhances segmentation accuracy significantly. This study intended to separate the oil spills of SAR images using U-NET and DeeplabV3 neural networks, separately with the lowest number of images and the highest accuracy possible. Each of these neural networks carries out image segmentation with different architectures independently and thus we could not combine these two networks for oil spill segmentation. We managed to find two accurate convolutional neural networks (CNNs) for oil spill segmentation because we did not have access to sufficient hardware facilities, like GPU, to train dozens of neural networks. The two networks we used in the present study are among the most well-known and widely used networks. Our purpose was to figure out which network was the best in SAR oil spill detection. Given the limited number of SAR oil spill images and as the input of CNNs needs many images for training, we increased the number of input images to 9801 using the augmentation technique. Then, we carefully identified oil spills with 300 epoch and a batch size of 5 using the Python programming language on the GoogleColab server. The oil spill detection accuracy was 78.8% in the U-NET network and 54% in the DeepLabV3 network. Accordingly, we conclude that the most accurate identification of SAR oil spills in images belong to the U-NET network.

Oil Spill SAR Image Segmentation via Probability Distribution Modeling

Segmentation of marine oil spills in synthetic aperture radar (SAR) images is a challenging task because of the complexity and irregularities in SAR images. In this work, we aim to develop an effective segmentation method which addresses marine oil spill identification in SAR images by investigating the distribution representation of SAR images. To seek effective oil spill segmentation, we revisit the SAR imaging mechanism in order to attain the probability distribution representation of oil spill SAR images, in which the characteristics of SAR images are properly modelled. We then exploit the distribution representation to formulate the segmentation energy functional, by which oil spill characteristics are incorporated to guide oil spill segmentation. Moreover, the oil spill segmentation model contains the oil spill contour regularization term and the updated level set regularization term which enhance the representational power of the segmentation energy functional. Benefiting from the synchronization of SAR image representation and oil spill segmentation, our proposed method establishes an effective oil spill segmentation framework. Experimental evaluations demonstrate the effectiveness of our proposed segmentation framework for different types of marine oil spill SAR image segmentation.

OILSPILL AND LOOK-ALIKE SPOTS FROM SAR IMAGERY USING OTSU METHOD AND ARTIFICIAL NEURAL NETWORK

Oil spill pollution plays a significant role in damaging marine ecosystem. Discharge of oil due to tanker accidents has the most dangerous effects on marine environment. The main waste source is the ship based operational discharges. Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. One major advantage of SAR is that it can generate imagery under all weather conditions. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish ‘oil spills’ from ‘look-alikes’. The features of detected dark spot are then extracted and classified to discriminate oil spills from look-alikes. This paper describes the development of a new approach to SAR oil spill detection using Segmentation method and Artificial Neural Networks (ANN). A SAR-based oil-spill detection process consists of three stages: image segmentation, feature extraction and object recognition (classification) of the segmented objects as oil spills or look-alikes. The image segmentation was performed with Otsu method. Classification has been done using Back Propagation Network and this network classifies objects into oil spills or look-alikes according to their feature parameters. Improved results have been achieved for the discrimination of oil spills and look-alikes.

Analysis of impacting factors on polarimetric SAR oil spill detection

Polarimetric synthetic aperture radar (SAR) oil spill detection parameters conformity coefficient (μ), Muller matrix parameters (|C|, B0), the eigenvalues of simplified coherency matrix (λnos) and the influence of SAR observing parameters, ocean environment and noise level are investigated. Radarsat-2 data are used to make systematic analysis of polarimetric parameters for different incidences, wind speeds, noise levels and the ocean phenomena (oil slick and look likes). The influence of the SAR observing parameters, the ocean environment and the noise level on the typical polarimetric SAR parameter conformity coefficient has been analyzed. The results indicate that conformity coefficient cannot be simply used for oil spill detection, which represents the image signal to the noise level to some extent. When the signals are below the noise level for the oil slick and the look likes, the conformity coefficients are negative; while the signals above the noise level corresponds to positive conformity coefficients. For dark patches (low wind and biogenic slick) with the signal below the noise, polarization features such as conformity coefficient cannot separate them with oil slick. For the signal above the noise, the oil slick, the look likes (low wind and biogenic slick) and clean sea all have positive conformity coefficients, among which, the oil slick has the smallest conformity coefficient, the look likes the second, and the clean sea the largest value. For polarimetric SAR data oil spill detection, the noise plays a significant role. So the polarimetric SAR data oil spill detection should be carried out on the basis of noise consideration.

Segmenting Oil Spills from Blurry Images Based on Alternating Direction Method of Multipliers

We exploit the alternating direction method of multipliers (ADMM) for developing an oil spill segmentation method, which effectively detects oil spill regions in blurry synthetic aperture radar (SAR) images. We commence by constructing energy functionals for SAR image deblurring and oil spill segmentation separately. We then integrate the two energy functionals into one overall energy functional subject to a linear mapping constraint that correlates the deblurred image and the segmentation indicator. The overall energy functional along with the linear constraint follows the form of ADMM and thus enables an effective augmented Lagrangian optimization. Furthermore, the iterative updates in the ADMM maintain information exchanges between the energy minimizations for SAR image deblurring and oil spill segmentation. Most existing blurry image segmentation strategies tend to consider deblurring and segmentation as two independent procedures with no interactions, and the operation of deblurring is thus not guided for obtaining an accurate segmentation. In contrast, we integrate deblurring and segmentation into one overall energy minimization framework with information exchanges between the two procedures. Therefore, the deblurring procedure is inclined to operate in favor of the more accurate oil spill segmentation. Experimental evaluations validate that our framework outperforms the separate deblurring and segmentation strategy for detecting oil spill regions in blurry SAR images.

Satellite Oil Spill Detection Using Artificial Neural Networks

Oil spills represent a major threat to ocean ecosystems and their health. Illicit pollution requires continuous monitoring and satellite remote sensing technology represents an attractive option for operational oil spill detection. Previous studies have shown that active microwave satellite sensors, particularly Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish oil spills from `look-alikes'. This paper describes the development of a new approach to SAR oil spill detection employing two different Artificial Neural Networks (ANN), used in sequence. The first ANN segments a SAR image to identify pixels belonging to candidate oil spill features. A set of statistical feature parameters are then extracted and used to drive a second ANN which classifies objects into oil spills or look-alikes. The proposed algorithm was trained using 97 ERS-2 SAR and ENVSAT ASAR images of individual verified oil spills or/and look-alikes. The algorithm was validated using a large dataset comprising full-swath images and correctly identified 91.6% of reported oil spills and 98.3% of look-alike phenomena. The segmentation stage of the new technique outperformed the established edge detection and adaptive thresholding approaches. An analysis of feature descriptors highlighted the importance of image gradient information in the classification stage.

A Physically Consistent Speckle Model for Marine SLC SAR Images

A new physically based speckle model for marine single-look complex (SLC) synthetic aperture radar (SAR) images is here presented and investigated. The model allows using full-resolution SAR images instead of multilook SAR images, in which, at the expense of a coarser spatial resolution, the speckle is mitigated. The model is based on the three-parameters generalized-K (GK) probability density function (pdf). GK pdf is a suitable physically-based speckle model for marine SAR images ensuring a continuous and physically consistent transition among different scattering scenarios. This speckle model embodies Rayleigh, K, and Rice scattering scenes which are typical of marine scenes. The use of the three parameters, related to the GK pdf ones, is able to highlight the presence of both low backscattering areas and areas in which a small dominant scatterer is present. This is operationally interesting in SAR oil spill detection procedures.

Synthetic Aperture Radar Oil Spill Segmentation by Stochastic Complexity Minimization

We present a new algorithm for oil spill segmentation in synthetic aperture radar (SAR) images, using the minimum description length (MDL) principle and a polygonal active grid. This algorithm is based on two steps: a first partitioning step into homogeneous regions and a second classification step with an automatic MDL thresholding. The obtained method allows one to segment the different candidate oil spills in an image automatically and in a few seconds.

Satellite SAR oil spill detection using wind history information

Due to its large coverage, satellite based SAR (Synthetic Aperture Radar) has proven very useful in large scale oil spill detection and monitoring. However, radar backscatter values from oil spills are not unique. Other phenomena may dampen out the capillary and short gravity waves which are sensed by the radar, and cause dark areas (generally called slicks) in the SAR imagery. At present, operational satellite SAR oil spill detection systems are based on an operator's experience in slick classification. Discrimination between oil and lookalikes includes analysis of shape, size,dB-values, gradients, texture, instantaneous wind, currents, platform-, ship lane- and natural seepage locations. However, data showing the temporal evolvement of the wind (i.e. the wind history) during the day(s) before SAR imaging, may also contribute significantly in the interpretation process. The geometrical shape of the slick is matched against wind history to estimate slick age. A procedure for such use of wind history in SAR oil spill detection is described by simple flow charts, and some examples are discussed. The method is in practical use at Tromso Satellite Station.