The Extended Bragg Scattering Model-Based Method for Ship and Oil-Spill Observation Using Compact Polarimetric SAR

Yin, Junjun; Yang, Jian; Zhou, Zhenxing; Song, Jianshe

doi:10.1109/jstars.2014.2359141

Cited by 74 publications

(49 citation statements)

References 30 publications

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“…This ensures a swath width significantly larger than the one provided by a fully polarimetric SAR system, with polarimetric performance that tends to the fully polarimetric one. This technological progress boosted the development of new techniques to exploit CP SAR for sea oil slick monitoring purposes (Shirvany, Chabert, and Tourneret 2012;Nunziata, Migliaccio, and Li 2015;Salberg, Rudjord, and Solberg 2014;Paes, Nunziata, and Migliaccio, forthcoming;Nunziata and Migliaccio 2014;Yin et al, forthcoming;Li et al 2015).…”

Section: Introductionmentioning

confidence: 99%

SAR polarimetry for sea oil slick observation

Migliaccio

Nunziata

Buono

2015

International Journal of Remote Sensing

130

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In this article, a review of polarimetric synthetic aperture radar (SAR) methods for sea oil slick observation is presented. Marine oil pollution monitoring is a topic of great applicative and scientific relevance. In this framework, the use of remotely sensed measurements is of special interest and, in particular, the SAR because of its almost all-weather and all-day imaging capability at fine spatial resolution is the most effective tool. Conventional single-polarization SAR oil spill monitoring techniques are limited in their capability to detect oil slicks since they strongly rely on suitable thresholds, training samples, and ancillary information. Hence, an expert image analyst is due. The launch of a number of polarimetric SAR missions, along with the understanding of the peculiar physical mechanisms governing the scattering by an oil slick, led to a new paradigm (known as physical processing) that fostered a set of polarimetric algorithms particularly robust and efficient. Hence, suitable polarimetric models that exploit the departure from the slick-free sea Bragg scattering have been developed to effectively address oil slick monitoring. A set of polarimetric features extracted following such electromagnetic models have been proved to be reliable for oil slick monitoring. Polarimetric SAR observations led to a significant improvement in sea oil slick observation since they allow distinguishing oil slicks from a broad class of lookalikes in an unsupervised way. In addition, deeper information on the damping properties of the pollutant can be also inferred, which is of paramount importance for remediation purposes. Such physical processing has been demonstrated to be robust at variance of microwave carrier frequency, e.g. L-, C-, and X-band, and suitable to be exploited to extract information by dual-polarized, full-polarized, and compact modes. All these make such a physical approach of operational interest since it is able to exploit a larger set of SAR measurements building up a virtual constellation. In this review all these are detailed.

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Section: Introductionmentioning

confidence: 99%

SAR polarimetry for sea oil slick observation

Migliaccio

Nunziata

Buono

2015

International Journal of Remote Sensing

130

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“…farm target and the ocean background and was stable under incident angles from 18° to 45° [29]. Yin deduced three parameters for ship and oil detection based on the X-Bragg model and proved their ability in discriminating targets from lookalikes [30]. These studies explored the feasibility of CP SAR in ship detection and became foundations for future practical applications.…”

Section: Methodsmentioning

confidence: 93%

“…Touzi [8] explored the polarization information in ship detection and found out that background and was stable under incident angles from 18 • to 45 • [29]. Yin deduced three parameters for ship and oil detection based on the X-Bragg model and proved their ability in discriminating targets from lookalikes [30].…”

Section: Introductionmentioning

confidence: 99%

Compact Polarimetric SAR Ship Detection with m-δ Decomposition Using Visual Attention Model

Zhang

Wang

et al. 2016

Remote Sensing

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Abstract:A few previous studies have illustrated the potentials of compact polarimetric Synthetic Aperture Radar (CP SAR) in ship detection. In this paper, we design a ship detection algorithm of CP SAR from the perspective of computer vision. A ship detection algorithm using the pulsed cosine transform (PCT) visual attention model is proposed to suppress background clutter and highlight conspicuous ship targets. It is the first time that a visual attention model is introduced to CP SAR application. The proposed algorithm is a quick and complete framework for practical use. Polarimetric features-the relative phase δ and volume scattering component-are extracted from m-δ decomposition to eliminate false alarms and modify the PCT model. The constant false alarm rate (CFAR) algorithm based on lognormal distribution is adopted to detect ship targets, after a clutter distribution fitting procedure of the modified saliency map. The proposed method is then tested on three simulated circular-transmit-linear-receive (CTLR) mode images, which covering East Sea of China. Compared with the detection results of SPAN and the saliency map with only single-channel amplitude, the proposed method achieves the highest detection rates and the lowest misidentification rate and highest figure of merit, proving the effectiveness of polarimetric information of compact polarimetric SAR ship detection and the enhancement from the visual attention model.

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“…Some previous studies explored the possibility of taking advantage of dual-and compact polarimetric SAR data to classify oil spills and biogenic slicks [27][28][29]. However, there are seldom quantitative comparisons of different polarimetric SAR modes, and their performance for actual oil spill classification applications.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images

Zhang

Liang

et al. 2017

Applied Sciences

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Abstract:In this paper, we present a comparison between several algorithms for oil spill classifications using fully and compact polarimetric SAR images. Oil spill is considered as one of the most significant sources of marine pollution. As a major difficulty of SAR-based oil spill detection algorithms is the classification between mineral and biogenic oil, we focus on quantitatively analyzing and comparing fully and compact polarimetric satellite synthetic aperture radar (SAR) modes to detect hydrocarbon slicks over the sea surface, discriminating them from weak-damping surfactants, such as biogenic slicks. The experiment was conducted on quad-pol SAR data acquired during the Norwegian oil-on-water experiment in 2011. A universal procedure was used to extract the features from quad-, dual-and compact polarimetric SAR modes to rank different polarimetric SAR modes and common supervised classifiers. Among all the dual-and compact polarimetric SAR modes, the π/2 mode has the best performance. The best supervised classifiers vary and depended on whether sufficient polarimetric information can be obtained in each polarimetric mode. We also analyzed the influence of the number of polarimetric parameters considered as inputs for the supervised classifiers, onto the detection/discrimination performance. We discovered that a feature set with four features is sufficient for most polarimetric feature-based oil spill classifications. Moreover, dimension reduction algorithms, including principle component analysis (PCA) and the local linear embedding (LLE) algorithm, were employed to learn low dimensional and distinctive information from quad-polarimetric SAR features. The performance of the new feature sets has comparable performance in oil spill classification.

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The Extended Bragg Scattering Model-Based Method for Ship and Oil-Spill Observation Using Compact Polarimetric SAR

Cited by 74 publications

References 30 publications

SAR polarimetry for sea oil slick observation

SAR polarimetry for sea oil slick observation

Compact Polarimetric SAR Ship Detection with m-δ Decomposition Using Visual Attention Model

Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images

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