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B2: ID.10689 Oil Spill Monitoring
Analysis of benefits and pitfalls of satellite SAR for coastal area monitoring
1Università di Napoli Parthenope, Italy; 2Shanghai Ocean University, Shanghai, China;
This study aims at describing the outcomes of the Dragon-3 project no. 10689. The research subject covered during the project are related to coastal area monitoring and they include sea pollution, coastline extraction, ship detection, sea oil spill tracking, etc. The key remote sensing tool is the Synthetic Aperture Radar (SAR) that provides fine resolution images of the microwave reflectivity of the observed scene. However, the interpretation of SAR images is not at all straightforward and all the above-mentioned coastal area applications cannot be easily addressed using single-polarization SAR.
Hence, the main outcome of this project is investigating the capability of multi-polarization SAR measurements to generate added-vale product in the frame of coastal area management. As a matter of fact, polarimetric models have been developed to take full benefit of multi-polarization information in order to conceive robust and effective methods to deal with coastal area applications. The main results can be summarized as follows:
All this matter will be discussed using actual L-C and X-band SAR data collected over coastal area -.
 F. Nunziata, M. Migliaccio, X. Li, and X. Ding, “Coastline extraction using dual-polarimetric COSMO-SkyMed PingPong mode SAR data,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 1, pp. 104-108, 2014.
 X. Ding, F. Nunziata, X. Li and M. Migliaccio, “Performance analysis and validation of wa- terline extraction approaches using single- and dual-polarimetric SAR data,” IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing (JSTARS). In print.
 F. Nunziata, M. Migliaccio and X. Li, “Sea oil slick observation using hybrid-polarity SAR architecture,” IEEE Journal of Oceanic Engineering. In print.
 Y. Cheng, B. Liu, X. Li, F. Nunziata, Q. Xue, X. Ding, M. Migliaccio and W.G. Pichel, “Monitoring of oil spill trajectories with COSMO-SkyMed X-band SAR images and model simulation,” IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing (JSTARS), vol. 7, no. 7, pp. 2895-2901, 2014.
An Overview Of Polarimetric SAR-Based Monitoring Of Sea Oil Slicks
1Università Parthenope, Italy; 2Shanghai Ocean University, China; 3NOAA/NESDIS, USA;
Among all maritime applications, oil pollution monitoring is a hot topic from both an operational and scientific perspective. Marine oil pollution is mainly due to i) illegal polluters that discharge oil-waste products, ii) accidental oil spills from oil rigs/platforms, iii) natural oil seeps, i. e., stocks of hydrocarbons that naturally come from the bottom of the oceans .
In this context, the use of microwave remote sensing is of paramount relevance. In fact, the synthetic aperture radar (SAR) is characterized by an almost all-weather and all-day capability of imaging Earth’s surface offering fine spatial resolution and wide area coverage. The latter represent key requirements for an effective sea oil slick observation, that consists of i) detection of oil slicks over the sea surface, ii) discrimination between oil slicks and look-alikes, i. e., biogenic films, low-wind areas, ship’s wakes, etc., and iii) mapping of physical properties that characterize sea oil slicks, i. e., damping .
Considering single-polarization SAR, the use of intensity information only limits the detection capability and does not allow sea oil slick discrimination/characterization. This is due to the fact that single-polarization SAR-based approaches for sea oil slick monitoring mostly rely on supervised image processing techniques that require external information including suitable thresholds, training samples, ancillary data and/or trained personnel .
Nowadays, a large set of polarimetric SAR (POLSAR) data is available in a wide range of imaging modes, frequencies and polarizations. The physical information they offer on the observed scene allows developing new robust and efficient algorithms that are able to address all the above-mentioned tasks in an unsupervised way. The cabability of POLSAR-based approaches to perform sea oil slick observation relies on the fact that sea surface scattering, under low-to-moderate wind conditions and at intermediate incidence angles (AOI), is well described by the almost deterministic Bragg scattering model, while oil slicks are characterized by a completely different scattering behavior. Following the same rationale, a broad class of look-alikes, i. e., surfactants calling for weak-damping properties, can be automatically distinguished from oil slicks since they are characterized by a Bragg-like scattering mechanism. However, POLSAR-based approaches need suitable electromagnetic models to properly interpret the scattering mechanisms of the observed scene [1, 2].
Nevertheless, operational use of POLSAR architectures is limited due to the fact that fully-polarimetric (FP) architecture is characterized by reduced swath and limited range of acceptable AOI if compared to single-/dual-polarized SAR. The latter may be operationally attractive, but conventional HH-HV/VV-VH dual-polarized architectures do not offer additional information useful for sea oil slick observation with respect to single-polarization SAR [1, 3].
Recently, a new POLSAR generation has been developed, namely Compact-polarimetry (CP), that represents a good compromise between polarimetric information content and area coverage. They are coherent dual-polarized architectures that receive in an orthogonal H-V basis while transmitting a circularly-polarized or slant linearly-polarized wave, i. e., Hybrid-Polarity (HP) and p/4 mode, respectively [2, 3]. Although there exist space missions operating in CP mode and further space missions equipped with CP SAR are already planned for forthcoming years, actually a consistent and reliable SAR dataset of CP measurements collected over sea oil slicks is still not available .
CP SAR architectures promise obtaining performance very close to FP SAR for sea oil slick observation purposes, although the physical information they carry on represents only a subset of the polarimetric information provided by FP SAR . However, a common reference framework is needed to quantitatively assess the performance of CP architectures and to undertake a fair comparative analysis with FP SAR. Slight differences over both sea surface and oil slicks can be found between CP and FP SAR architectures and among CP mode, and they can be physically explained through the different mapping of FP-CP eigenvalues into the corresponding polarimetric observables, i. e., FP coherency matrix and CP wave coherency matrix.
1) Migliaccio, M., Nunziata, F. and Buono, A., SAR polarimetry for sea oil slick observation, Int. J. Remote Sens., vol. 36, no. 12, pp. 3243-3273, 2015.
2) Nunziata, F., Migliaccio, M., and Li, X., Sea oil slick observation using hybrid-polarity SAR architecture, J. Oceanic Eng., vol. 40, no. 2, pp. 426-440, 2015.
3) Cloude, S.R., Polarisation: applications in remote sensing, Oxford University Press, 2009.
4) Sabry, R., Vachon, P.W., A unified framework for general compact and quad polarimetric SAR data and imagery analysis, IEEE Trans. Geosci. Remote Sens., vol. 52, no. 1, pp. 582-602, 2014.
Multi-Frequency And Multi-Polarization Scattering Analysis For Model-Based Coastal Areas Classification
1Università Parthenope, Italy; 2Shanghai Ocean University, China; 3NOAA/NESDIS, USA;
Coastal areas represent worldwide key environments for economy, tourism, trading and society. Hence, a continuous and updated remotely observation is needed for retrieving information on the urbanization rate, agricultural land cover, environmental sustainability, etc. Synthetic aperture radar (SAR) allows providing synoptic maps of the observed scene with fine spatial resolution in almost any illumination and weather condition. In addition, in the last decade a large set of polarimetric SAR (POLSAR) data, collected at different wavelengths and polarizations, has been made available to take full benefit of the physical information they offer.
Several approaches based on POLSAR data have been proposed for classification purposes. Most of them rely on scattering models to characterize the physical mechanisms that rule the observed scene [1-3]. Model-based classifiers deal with different decomposition schemes of polarimetric observables in order to associate, for each pixel of the observed scene, the elementary scattering mechanisms that may occur [4-5]: i) surface scattering due to flat or slightly rough surfaces, ii) double-bounce scattering typical of the ground-buildings or ground-trunks interaction, 3) volume scattering related to completely random behavior or to inclusion into the observed layer, 4) helix scattering that characterizes non-reflection symmetric scenarios.
In this study, the coastal area of the Bohai sea where the Yellow River flows is analyzed. The Yellow River is the most sediment-filled river and the sixth-longest one in the world. It is characterized by a huge amount of silt and carried sediment, and it is affected by both natural and human-related activities including erosion, floods and pollution. The Yellow River is of paramount importance for safe navigation and local economy (farms, aquacultures). Nevertheless, it represents a challenging environment where different scenarios are present: forest, river, sea, urban, intertidal zones, beaches, swamps, ponds, etc.
In this work, a polarimetric study of the scattering properties that characterize the different scenarios of that coastal area is performed using actual L- and C-band fully-polarimetric (FP) SAR data in order to investigate their scattering properties. This can be done using suitable electromagnetic modeling and analysis tools. Such information extracted from multi-frequency POLSAR data is then combined together with backscattering information provided for different transmitted polarizations. This can be accounted for using conventional electromagnetic models that predict the backscattering coefficient for reference scenarios (rough surfaces, randomly oriented structures, etc.). Further, the modeling needs the knowledge of physical parameters that affects the backscattering coefficient through salinity, roughness, soil composition, etc. This approach allows improving scattering model-based classification results in case of classes characterized by the same polarimetric properties, i. e., sea and river, or beaches and intertidal areas. The analysis results are compared with optical images and ground truth data collected over the Yellow River delta during an in-situ ship-based campaign.
1) Freeman, A. and Durden, S., A three-component scattering model for polarimetric SAR data, IEEE Trans. Geosci. Remote Sens., vol. 36, no. 3, pp. 963-973, May 1998.
2) Freeman, A. and Durden, S., A three-component scattering model for polarimetric SAR data, IEEE Trans. Geosci. Remote Sens., vol. 36, no. 3, pp. 963-973, May 1998.
3) Jafari, M., Maghsoudi, Y., and Zoej, M.J.V., A New Method for Land Cover Characterization and Classification of Polarimetric SAR Data Using Polarimetric Signatures, IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens., vol. 8, no. 7, Jul. 2015.
4) Pottier, E. and Lee, J.-S., Polarimetric Radar Imaging From Basics to Applications, Boca Raton (FL), CRC Press, 2009.
5) Cloude, S. R. and Pottier, E., A review of target decomposition theorems in radar polarimetry, IEEE Trans. Geosci. Remote Sens., vol. 34, no. 2, pp. 498-518, Mar. 1996.
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Conference: 2016 Dragon 3 Final Results Symposium
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