Domenico Velotto

Dual-Polarized TerraSAR-X Data for Oil-Spill Observation

The paper presents a novel method to estimate the speed of a moving ship and the range velocity component of the current sea surface. The estimate of the ship speed is obtained by multilooking techniques [1]. The generation of a sequence of images from one single complex SAR image corresponds to an image time series with reduced resolution. This allows evaluating the velocity components in range and azimuth of the target by applying change detection techniques of the time series. The estimation of the displacement vector of a moving target in consecutive images of the sequence allows the estimation of the azimuth velocity component. The range velocity component is estimated by the variation of the signal amplitude in time. The results are applied on TerraSAR-X StripMap and High-Resolution SpotLight data and validated by Automatic Identification System (AIS). Furthermore the method is applied in order to estimate the range component of the current speed. The measurements are compared to model results of Federal Maritime and Hydrographic Agency of Germany (BSH) and Federal Waterways Engineering and Research Institute (BAW).In this study to investigate the capability of high resolutionSAR data to detect small quantities of oil near offshore platforms, a data set acquired both in single and dual-polarization mode over the Ekofisk oil platform is analyzed. Instantaneous wind information retrieved directly from the SAR image using the XMOD Geophysical Model Function (GMF) and wind history from the DWD forecast global sea wave model (GSM) are provided to discriminate a probable wind shadowing effect near the offshore installation and estimate the age of the spills. Foremost examples from the data set acquired between January, 2008 and February, 2010 are shown here and discussed in detail.A study exploiting dual-polarimetric X-band synthetic aperture radar (SAR) data to observe oil at sea is undertaken for the first time. The polarimetric model exploits the interchannel correlation between the like polarized channels. Accordingly, two parameters related to the interchannel correlation, namely, the amplitude coherence and the copolarized phase difference (CPD) standard deviation, are accounted for, and their performances, with respect to sea oil slick observation, are carefully discussed. Single-look Slant range Complex dual-polarized TerraSAR-X SAR data, in which both certified oil slicks and weak-damping look-alikes are present, are used to verify the efficiency of the proposed approaches. Results show the advantage of the CPD approach and the effectiveness of TerraSAR-X dual-polarized products for such application.

Dual-Polarimetric TerraSAR-X SAR Data for Target at Sea Observation

A physical dual-polarimetric model to observe man-made metallic targets at sea in dual-polarimetric coherent X-band synthetic aperture radar (SAR) data is proposed. The model exploits the intrinsic different symmetry properties of man-made targets and sea surface and is tested over actual StripMap TerraSAR-X HH-HV and VV-VH dual-polarimetric SAR data and colocated ground truth measurements. Then, an operational physically based filter to observe targets at sea is proposed. The filter is very attractive in terms of both detection performances and processing time. A typical SAR scene is processed in seconds by a conventional PC processor.

Near real time monitoring of platform sourced pollution using TerraSAR-X over the North Sea

Continuous operational monitoring by means of remote sensing contributes significantly towards less occurrence of oil spills over European waters however, operational activities show regular occurrence of accidental and deliberate oil spills over the North Sea, particularly from offshore platform installations. Since the areas covered by oil spills are usually large and scattered over the North Sea, satellite remote sensing particularly Synthetic Aperture Radar (SAR) represents an effective tool for operational oil spill detection. This paper describes the development of a semi-automated approach for oil spill detection, optimized for near real time offshore platform sourced pollution monitoring context. Eight feature parameters are extracted from each segmented dark spot. The classification algorithm is based on artificial neural network. An initial evaluation of this methodology has been carried out on 156 TerraSAR-X images. Wind and current history information also have been analyzed for particular cases in order to evaluate their influences on spill trajectory.

First Comparison of Sentinel-1 and TerraSAR-X Data in the Framework of Maritime Targets Detection: South Italy Case

The Sentinel-1A is the first of two satellites that composes the Sentinel-1 radar mission. Both satellites operate a C-band synthetic aperture radar (SAR) system to give continuity to the European SAR program. SAR is a flexible sensor able to fulfil users/applications requirements in terms of resolution and coverage thanks to different operational modes and polarizations. With the in-orbit availability of very-high-resolution X-band SAR sensors, the Sentinel-1 satellites have been designed to achieve wide coverage at medium to high resolution. The interferometric wide swath (IWS) mode implemented with the terrain observation with progressive scan (TOPS) technique is the standard acquisition mode over European waters and land masses. IWS in dual-polarization (VV/VH) combination offers 250-km swath at 5 m × 20 m (range × azimuth) spatial resolution. These specifications are in line with the needs of the European Maritime and Security Agency (EMSA) for oil spill and ship detection applications included in the CleanSeaNet program. The main goals of this paper are: assessment of medium-to-high-resolution C-band Sentinel-1 data with very-high-resolution X-band TerraSAR-X data for maritime targets detection; synergetic use of multiplatforms satellite SAR data for target features extraction; evaluation of polarimetric target detectors for the available co-polarization and cross-polarization Sentinel-1A IWS VV/VH products. The objectives are achieved by means of real, almost coincident C-band and X-band SAR data acquired by Sentinel-1A and TerraSAR-X satellites over Gulf of Naples and Catania (South Italy). Furthermore, the obtained results are supported by recorded ground truth vessel reports via terrestrial automatic identification system (AIS) stations located in the area.

Neural networks for oil spill detection using TerraSAR-X data

The increased amount of available Synthetic Aperture Radar (SAR) images involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In this paper we present the potentialities of TerraSAR-X (TS-X) data and Neural Network algorithms for oil spills detection. The radar on board satellite TS-X provides X-band images with a resolution of up to 1m. Such resolution can be very effective in the monitoring of coastal areas to prevent sea oil pollution. The network input is a vector containing the values of a set of features characterizing an oil spill candidate. The network output gives the probability for the candidate to be a real oil spill. Candidates with a probability less than 50% are classified as look-alikes. The overall classification performances have been evaluated on a data set of 50 TS-X images containing more than 150 examples of certified oil spills and well-known look-alikes (e.g. low wind areas, wind shadows, biogenic films). The preliminary classification results are satisfactory with an overall detection accuracy above 80%.

Oil-slick observation using single look complex TerraSAR-X dual-polarized data

In this study single look complex (SSC) TerraSAR-X dual-polarized data are firstly exploited for sea oil slick observation purposes. An electromagnetic model which, based on the Co-polarised Phase Difference between the HH and VV channels (CPD), allows describing the X-band sea surface scattering with and without surface slicks is proposed.

A Combination of Traditional and Polarimetric Features for Oil Spill Detection Using TerraSAR-X

Synthetic aperture radar (SAR) images are operationally used for the detection of oil spills in the marine environment, as they are independent of sun light and weather-induced phenomena. Exploitation of radar polarimetric features for operational oil spill detection is relatively new and until recently those properties have not been extensively exploited. This paper describes the development of a oil spill detection processing chain using coherent dual-polarimetric (copolarized channels, i.e., HH-VV) TerraSAR-X images. The proposed methodology focuses on offshore platform monitoring and introduces for the first time a combination of traditional and polarimetric features for object-based oil spill detection and look-alike discrimination. A total number of 35 feature parameters were extracted from 225 oil spills and 26 look-alikes and divided into training and validation dataset. Mutual information content among extracted features have been assessed and feature parameters are ranked according to their ability to discriminate between oil spill and look-alike. Extracted features are used for training and validation of a support vector machine-based classifier. Performance estimation was carried out for the proposed methodology on a large dataset with overall classification accuracy of 90% oil spills and 80% for look-alikes. Polarimetric features such as geometric intensity, copolarization power ratio, span proved to be more discriminative than other polarimetric and traditional features.

Target classification in oceanographic SAR images with deep neural networks: Architecture and initial results

Synthetic Aperture Radar (SAR) provides detailed information of Oceans surface and man-made floating structures. Advances in SAR technology and deployment of new SAR satellites have contributed to an increasing number of remote sensing data available. Handle this large amount of data with human operators is infeasible. Therefore, the use of automated tools to process remote sensing images, identify regions of interest, and select relevant information are needed. The use of neural networks to solve SAR image classification problems is well known. A typical architecture consists of a shallow feed-forward neural network with an input layer, a hidden layer, and an output layer. This type of neural network, combined with back-propagation and a gradient-based training algorithm, is able to solve complex problems in SAR image analysis. However, this architecture is unable to take advantage of unlabeled data during its training process, and in many cases the input features need to be carefully tuned in order to reduce the overall network complexity. This paper proposes the application of Deep Neural Networks (DNN) to perform oceanographic-object classification.

Ship Classification in TerraSAR-X Images With Convolutional Neural Networks

Synthetic aperture radar (SAR) is an important instrument for oceanographic observations, providing detailed information of oceans’ surface and artificial floating structures. Due to advances in SAR technology and deployment of new SAR satellites, an increasing amount of data is available, and the development of efficient classification systems based on deep learning is possible. A deep neural network has improved the state of the art in classification tasks of optical images, but its use in SAR classification problems has been less exploited. In this paper, a full workflow for SAR maritime targets detection and classification on TerraSAR-X high-resolution image is presented, and convolutional neural networks (CNNs) recently proposed in the literature are cross evaluated on a common data set composed of five maritime classes, namely, cargo, tanker, windmill, platform, and harbor structure. Based on experiments and tests, a multiple input resolution CNN model is proposed and its performance is evaluated. Our results indicate that CNNs are efficient models to perform maritime target classification in SAR images, and the combination of different input resolutions in the CNN model improves its ability to derive features, increasing the overall classification score.

Spatial and Temporal Variations of Oil Spills in the North Sea Observed by the Satellite Constellation of TerraSAR-X and TanDEM-X

This study investigated the spatial and temporal variations of two groups of oil slicks with similar features that were observed in the North Sea by spaceborne synthetic aperture radar (SAR) TerraSAR-X (TSX) and TanDEM-X (TDX) within 13 h of each other, which were operating in the satellite constellation. Based on the SAR observations and the General NOAA Operational Modeling Environment (GNOME) simulations of the oil trajectories, it is unlikely that the oil spills observed by TSX on August 21, 2012 entirely drifted from the oil spills observed by TDX on August 20. Rather, the oil slicks observed after 13 h were likely composed of two parts. One part was the new oil spills that started to leak sometime after the first SAR acquisition on August 20 and that were subsequently observed on August 21. The other part was the older oil spills that had drifted from the original oil slicks observed on August 20 by TDX. The fast weathering of light crude oil and the mixture of oil and water produced in the Forties platforms should contribute to the rapid dispersion and dissipation of the previously observed oil slicks. The findings of this study contribute to our understanding of the life history of oil spill on the sea surface around offshore drilling platforms.