Intelligent hybrid system for dark spot detection using SAR data

Damping Bragg scattering from the ocean surface is the basic underlying principle of synthetic aperture radar (SAR) oil slick detection, and they produce dark spots on SAR images. Dark spot detection is the first step in oil spill detection, which affects the accuracy of oil spill detection. However, some natural phenomena (such as waves, ocean currents, and low wind belts, as well as human factors) may change the backscatter intensity on the surface of the sea, resulting in uneven intensity, high noise, and blurred boundaries of oil slicks or lookalikes. In this paper, Segnet is used as a semantic segmentation model to detect dark spots in oil spill areas. The proposed method is applied to a data set of 4200 from five original SAR images of an oil spill. The effectiveness of the method is demonstrated through the comparison with fully convolutional networks (FCN), an initiator of semantic segmentation models, and some other segmentation methods. It is here observed that the proposed method can not only accurately identify the dark spots in SAR images, but also show a higher robustness under high noise and fuzzy boundary conditions.

An efficient dark spot detection method for offshore oil spill in SAR images based on edge-enhanced attention fusion.

A new automated methodology for oil spill detection is presented, by which full synthetic aperture radar (SAR) high‐resolution image scenes can be processed. The methodology relies on the object‐oriented approach and profits from image segmentation techniques to detected dark formations. The detection of dark formations is based on a threshold definition that is fully adaptive to local contrast and brightness of large image segments. For the detection process, two empirical formulas are developed that also permit the classification of oil spills according to their brightness. A fuzzy classification method is used to classify dark formations as oil spills or look‐alikes. Dark formations are not isolated and features of both dark areas and sea environment are considered. Various sea environments that affect oil spill shape and boundaries are grouped in two knowledge bases, used for the classification of dark formations. The accuracy of the method for the 12 SAR images used is 99.5% for the class of oil spills, and 98.8% for that of look‐alikes. Fresh oil spills, fresh spills affected by natural phenomena, oil spills without clear stripping, small linear oil spills, oil spills with broken parts and amorphous oil spills can be successfully detected.

In order to obtain the higher classification accuracy in specific categories for the different feature subset, a hierarchical classification algorithm based on Feature Selection is proposed, and is used for synthetic aperture radar (SAR) image classification, and feature selection is achieved by Genetic algorithm. The algorithm has two main characteristics: one is hierarchical classification which consists of many two-class classifier, and the two-class classifier is trained by the optimal feature subset which is selected according to different categories; the second is the classifier of support vector machine SVM (Support Vector Machine); the two is Genetic algorithm which can search out the optimal feature subset and parameters of support vector machine that is most suitable for the category, by unified coding the feature set and the parameters of SVM to constitute the Chromosome. The experiment on the first polarimetric SAR data show that the algorithm can obtain higher classification accuracy rate.

The authors study the performance of automatic methods for oil spill detection in ERS SAR images. The presented algorithm has three main parts: (i) detection of dark spots; (ii) feature extraction; and (iii) dark spot classification. The dark spot detection locates all spots which can possibly be oil slicks in the image. For each slick, a set of backscatter, textural, and geometrical features are extracted. The dark spots are then classified into possible oil slicks and look-alikes based on the extracted features. Based on the current study, the authors believe that a semi-automatic oil slick identification system which can discriminate between oil slicks and look-alikes can be developed. To achieve this, some new features describing the surroundings of a slick and the slick itself must be defined and tested.

Automatic classification of the electrocardiogram (ECG) signals is an important subject for clinical diagnosis of heart disease. This study investigates the design of a high-efficient system to classify five types of ECG beat namely normal beats and four manifestations of heart arrhythmia, in twofold. First, we propose a system that includes two main modules: a feature extraction module and a classification module. Feature extraction module extracts a suitable combination of the ECG's morphological characteristics and timing interval features. Discrete wavelet transform is used to extract the morphological features. In the classification module, a multi-class support vector machine (SVM)-based classifier is employed. The parameters of this system are determined based on a trial and error method and its performance is evaluated for the MIT-BIH arrhythmia database. Extensive experiments on the parameters of this system such as classifier kernels and various types of features are conducted. These experiments show that in SVM training, the kernels, kernel parameters, and feature selection have very important roles for SVM classification accuracy. Therefore, most appropriates of these parameters should be used for SVM training. Then at the second fold, a novel hybrid intelligent system (HIS) is proposed that consists of three main modules. In the HIS, further to the two mentioned modules, an optimization module is added. In this module, a genetic algorithm is used for optimization of the relevant parameters of system. These parameters are: wavelet filter type for feature extraction, wavelet decomposition level, and classifier's parameters. Experimental results show that optimization improves the recognition system, efficiently, and HIS is more superior to the system, which as constant parameters.

Oil spill has tremendous effect on marine environment. Therefore, it is significant for protecting marine environment to get oil spill information timely. Presently, the methods which most countries adopt to detect oil spill are mainly direct detection or remote sensing methods. Among these methods, Synthetic Aperture Radar (SAR), one technique of remote sensing methods, has been becoming a hot research field nowadays. This paper concludes main characters of SAR images in oil spill detection. It introduces, compares and analyses the oil spill detection steps and realized techniques. There are four steps in oil spill detection process, including filtering, target detection, feature extraction and classification. Filtering can be completed by several filtering techniques, such as Lee, advanced Lee and Frost. General speaking, there are four methods can realize target detection, which are single threshold, adaptive threshold, wavelet transform and max entropy method. About the third step, feature extraction, 12 features can be used to represent oil spill. Classification is a key and difficult step in oil spill detection, in which algorithms based on Bayes and neural network can be adopted. Finally, the paper points out the development direction of oil spill detection in SAR images.

Oil spill is considered one of the main threats to marine and coastal environments. Efficient monitoring and early identification of oil slicks are vital for the corresponding authorities to react expediently, confine the environmental pollution and avoid further damage. Synthetic aperture radar (SAR) sensors are commonly used for this objective due to their capability for operating efficiently regardless of the weather and illumination conditions. Black spots probably related to oil spills can be clearly captured by SAR sensors, yet their discrimination from look-alikes poses a challenging objective. A variety of different methods have been proposed to automatically detect and classify these dark spots. Most of them employ custom-made datasets posing results as non-comparable. Moreover, in most cases, a single label is assigned to the entire SAR image resulting in a difficulties when manipulating complex scenarios or extracting further information from the depicted content. To overcome these limitations, semantic segmentation with deep convolutional neural networks (DCNNs) is proposed as an efficient approach. Moreover, a publicly available SAR image dataset is introduced, aiming to consist a benchmark for future oil spill detection methods. The presented dataset is employed to review the performance of well-known DCNN segmentation models in the specific task. DeepLabv3+ presented the best performance, in terms of test set accuracy and related inference time. Furthermore, the complex nature of the specific problem, especially due to the challenging task of discriminating oil spills and look-alikes is discussed and illustrated, utilizing the introduced dataset. Results imply that DCNN segmentation models, trained and evaluated on the provided dataset, can be utilized to implement efficient oil spill detectors. Current work is expected to contribute significantly to the future research activity regarding oil spill identification and SAR image processing.

Image segmentation is a popular application area of machine learning. In this context, each target region drawn from an image is defined as a class towards recognition of instances that belong to this region (class). In order to train classifiers that recognize the target region to which an instance belongs, it is important to extract and select features relevant to the region. In traditional machine learning, all features extracted from different regions are simply used together to form a single feature set for training classifiers, and feature selection is usually designed to evaluate the capability of each feature or feature subset in discriminating one class from other classes. However, it is possible that some features are only relevant to one class but irrelevant to all the other classes. From this point of view, it is necessary to undertake feature selection for each specific class, i.e, a relevant feature subset is selected for each specific class. In this paper, we propose the so-called multi-task feature selection approach for identifying features relevant to each target region towards effective image segmentation. This way of feature selection requires to transform a multi-class classification task into $n$ binary classification tasks, where $n$ is the number of classes. In particular, the Prism algorithm is used to produce a set of rules for class specific feature selection and the K nearest neighbour algorithm is used for training a classifier on a feature subset selected for each class. The experimental results show that the multi-task feature selection approach leads to an significant improvement of classification performance comparing with traditional feature selection approaches.

In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

Polarimetric Synthetic Aperture Radar (SAR) has powerful capability in marine oil spill detection since it could distinguish clean sea surface from mineral oil spill and false alarms as biogenic thin films. As an alternative to fully polarimetric (FP) SARs, which provide complete scattering information, compact polarimetric (CP) SARs transmit a polarized signal different from conventional horizontal and vertical ones, while receiving coherently the backscattered signal. Hence, they could obtain partial polarimetric information while maintaining a large swath-width, which makes it an ideal tool for marine oil spills observation. However, the proper selection of compact polarimetric SAR modes still remains a critical issue. By transmitting SAR signal with different polarizations, the scattering mechanism of the sea surface can be reflected by different CP SAR features. Hence, it is necessary to analyze the behavior of sea surface scattering as experienced by different CP SAR modes, i. e., different transmitted polarization. In this paper, a unified framework was applied to derive compact polarimetric SAR features under General Transmit and Linear Receive (GTLR) polarization configurations. Following the rationale of polarization signature, the characteristics of features derived by transmitting signals under different roll and ellipticity angles are analyzed. Experiments were conducted on a UAVSAR (Uninhabited Aerial Vehicle SAR) FP SAR image acquired at L-band during an oil-on-water exercise conducted offshore the Norwegian coast, where plant, crude oil, and oil emulsions with different oil-water mixtures were released at sea.

Abstract. The use of synthetic aperture radar (SAR) to investigate the ocean surface provides a wealth of useful information that is very seldom used to its full potential. Here we will discuss the application of multifractal techniques to detect oil spills and the dynamic state of the sea regarding turbulent diffusion. We present different techniques in order to relate the shape of the multifractal spectral functions and the maximum fractal dimension to the behaviour of the ocean surface. We compare eddy and sheared dominated flows with convective driven flows and discuss the different features and observation methods. We also compare the scaling of different oil spills detected by means of SAR images. Recent spills and weathered ones are selected and compared to investigate their behaviour in different spatial and temporal ranges. We calculate the partition function based on the grey intensity value of each SAR pixel deriving several types of multifractal spectra as a function of spill residence time estimated for each image. Image manipulations are seen to reduce the speckle noise and thus distinguish much better the texture of the oil spill images. The results are used to discuss how eddy diffusivity may be estimated and used in a description of the ocean surface using a simple turbulence kinematic simulation model to predict the shape of oil spills. Differences in the multifractal spectrum among SAR images may detect the slicks due to plankton and also provide information on the age of the oil spills, on the Lagrangian turbulent structure and on ocean surface diffusivity.

1141194 During oil spills response activities (OSR), discrimination and characterization of oil slicks has the potential to minimize false alarms and to indicate the location of thicker layers of oil, providing tactical and strategic information to decision makers. This specific demand for spatial intelligence during emergencies emphasizes the importance of the Multisource Image Processing System developed by the Brazilian Institute for Space Research (INPE) and described herein. Synthetic Aperture Radars (SAR) are the most common operational remote sensing data providers, acquiring images in different frequencies, incidence angles, resolutions, polarizations and formats (amplitude, intensity or complex). The system is able to integrate this product diversity including polarized (SAR), polarimetric (PolSAR) and optical data acquired by multiple sensors with different signal to noise ratios (SNR) and statistical distributions. The classifier uses a supervised approach to compare stochastic distances between different statistical distributions and statistical hypothesis testing to associate confidence levels to the results. Two study cases including mineral and biogenic oils with internal thickness variations were used to demonstrate the potential of the system to process SAR and PolSAR data, considering different formats and statistical distributions. The system was able to differentiate mineral oils from biogenic oils, as well as to detect thicker layers within the oil slicks with high global accuracies and low uncertainties. The polarimetric (dual and full-pol) and the intensity pair data had better accuracy at discriminating different types of oils; meanwhile SAR and PolSAR data returned statistically equivalent global accuracies in characterizing oil slicks. The low noise floor in UAVSAR sensor is able to detect oil thickness variations, which is feasible even if only using the VV channel. The possibility to qualitatively identify the thicker layers of the spilled oil, as well as minimize false alarms ratio is a significant contribution to ongoing efforts to improve OSR. The classification maps provided by the system may also be used to extract the relative areas for each type of oil, supporting the oil volume estimation. In this context, the integration of all information extracted from SAR and PolSAR data is useful both at strategic and tactical levels of an emergency, and is being used to guide aerial surveillance and the deployment of barriers and skimmers, aiming to increase oil recovery efficiency and minimize environmental impact.

Reliable and accurate land use/land cover (LULC) map is a crucial data source for the understanding of coupled human–environment systems, monitoring changes, timely low-cost planning, and management of natural resources. Improvements in sensor technologies and machine learning capabilities have shifted the attention of remote sensing community to data complementarity through fusion of multi-sensor data for accurate feature extraction and mapping. Amalgamation of optical and synthetic aperture radar (SAR) images has shown promising advantages in enhancing the accuracy of extracting LULC as such method allows exploitation of information in sensors. This study investigated the potential of using freely available multisource Sentinel images to extract LULC maps in semi-arid environments through multi-scale geographic object-based image analysis (GEOBIA). A multi-scale classification framework that integrates GEOBIA, correlation-based feature selection (CFS), and random forest (RF)-supervised classification was adopted to extract LULC from assimilation of Sentinel multi-sensor products. First, Sentinel-1 and -2 images were pre-processed. Second, optimum multi-scale segmentation levels were selected using F-score segmentation quality measures. Third, 70 features of various spectral indices and derivatives and geometrical features from optical data and multiple ratios and textural features from dual-polarization SAR images were computed, and a CFS based on wrapper approach was used to select the most significant features at multi-scale levels. Finally, a single and multi-scale RF classifier was used to extract LULC classes using the most relevant features extracted from Sentinel SAR and optical images. Results of multi-scale image segmentation optimization showed that scale parameter (SP) values of 40, 60, and 150 were optimal for extraction of LULC classes. Results of feature selection showed that 22, 24, and 27 features were selected at scale SP values of 40, 60, and 150, respectively. Half of the features were common among the three scales. Single RF classification yielded overall accuracy (OA) values of 92.10%, 93%, and 91% and kappa coefficients of 0.901, 0.912, and 0.89 at scale values of 150, 60, and 40, respectively. Multiscale RF classification from scale values of 150 and 60 produced better LULC classification with OA 96.06% and kappa coefficient of 0.95 compared with other scale SP values. The integrated approach demonstrated an effective and promising method for high-quality LULC extraction from coupling optical and SAR images. Overall, multi-sensor Sentinel images along with the adopted approach feature a remarkable potential for improving LULC extraction and can effectively be used to update geographic information system layers for various applications.

Oil spill is an environmental challenthat influences various aspects of coastal zones including humans, health, economy, and coastal ecosystems. The source of the oil spill can be both natural and anthropogenic. Oil spill response is dependent on the prompt detection of spillain open waters and at the coast. This study uses Sentinel-1 (S-1) C-band Synthetic Aperture Radar (SAR) data to detect oil spill that transpired at Karachi’s coastline from 23 to 26 October 2018. The oil spill detection was conducted using the K-means clustering algorithm of S-1 SAR data that involves reduction of speckle. Meteorological data consisting of wind speed and direction from S-1 Level-2 Ocean product (L2 OCN) based on the European Centre for Medium-RanWeather Forecasts (ECMWF) was also utilized. Initially, two long streaks of bunker oil covering an approximate area of 9.45 km2 was detected on 23 October 2018. Later oil spill spread over an area of ∼188.30 km2 (∼20 times more) until 26 October 2018 along the Karachi shoreline. Spilled oil covered ∼20 times more of its first spread area at an alarming rate of ∼37.63 km2 per day due to wind and wave action. Oil spill damaged the local ecology along the western coast of Karachi. Such damages can be avoided by installing an oil spill monitoring system using SAR and ground data which will support both environment and economy.