Multiscale oil slick segmentation with Markov chain model

Mercier, Grégoire; Derrode, Stéphane; Pieczynski, Wojciech; Caillec, J.-M. Le; Garello, R.

doi:10.1109/igarss.2003.1294834

Cited by 16 publications

(9 citation statements)

References 9 publications

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“…A specific kernel is developed to perform accurate segmentation of the local sea-surface wave spectrum by using both radiometric and texture information. Instead of the previous studies such as in [37] and [38], this technique is efficient in an operational context and proves to be a relevant strategy to detect oil slicks as abnormal situations of the seasurface roughness and radiometry.…”

Section: Discussionmentioning

confidence: 99%

Partially Supervised Oil-Slick Detection by SAR Imagery Using Kernel Expansion

Mercier

Girard-Ardhuin

2006

IEEE Trans. Geosci. Remote Sensing

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Abstract-Spaceborne synthetic aperture radar (SAR) is well adapted to detect ocean pollution independently from daily or weather conditions. In fact, oil slicks have a specific impact on ocean wave spectra. Initial wave spectra may be characterized by three kinds of waves, namely big, medium, and small, which correspond physically to gravity and gravity-capillary waves. The increase of viscosity, due to the presence of oil damps gravitycapillary waves. This induces not only a damping of the backscattering to the sensor but also a damping of the energy of the wave spectra. Thus, local segmentation of wave spectra may be achieved by the segmentation of a multiscale decomposition of the original SAR image. In this paper, a semisupervised oil-slick detection is proposed by using a kernel-based abnormal detection into the wavelet decomposition of a SAR image. It performs accurate detection with no consideration to signal stationarity nor to the presence of strong backscatters (such as a ship). The algorithm has been applied on ENVISAT Advanced SAR images. It yields accurate segmentation results even for small slicks, with a very limited number of false alarms.

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Section: Discussionmentioning

confidence: 99%

Partially Supervised Oil-Slick Detection by SAR Imagery Using Kernel Expansion

Mercier

Girard-Ardhuin

2006

IEEE Trans. Geosci. Remote Sensing

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“…In other cases we think that it is better to use all the available information, that is, maximizing the number of available samples by using increasing window sizes. Nevertheless, pyramidal multiscale decompositions can also be useful in the case of phenomena characterizations (see [9] for example).…”

Section: Introductionmentioning

confidence: 99%

A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis

Inglada

Mercier

2007

IEEE Trans. Geosci. Remote Sensing

464

253

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In this paper, we present a new similarity measure for automatic change detection in multitemporal SAR images. This measure is based on the evolution of the local statistics of the image between two dates. The local statistics are estimated by using a cumulant-based series expansion which approximates the probability density functions in the neighborhood of each pixel in the image. The degree of evolution of the local statistics is measured using the Kullback-Leibler divergence. An analytical expression for this detector is given, allowing a simple computation which depends on the 4 first statistical moments of the pixels inside the analysis window only. The proposed change indicator is compared to the classical mean ratio detector and also to other model-based approaches. Tests on simulated and real data show that our detector outperforms all the others. The fast computation of the proposed detector allows a multiscale approach in change detection for operational use. The so-called multiscale change profile (MCP) is introduced to yield change information on a wide range of scales and to better characterize the appropriate scale. Two simple yet useful examples of applications show that the MCP allows the design of change indicators which provide better results than a monoscale analysis.

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“…Wavelets transforms are first used, and a Markov chain model is applied for the slick segmentation [3]. Then a modified morphological pyramid is included to the fuzzy c-mean algorithm for the refinement of tbe detection results [4].…”

Section: Introductionmentioning

confidence: 99%

Sea surface slicks characterization in SAR images

Kanaa¹,

Mercier²,

Tonyé³

2005

Europe Oceans 2005

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Grmoire. Mercie@,enstbretame.fi. Ecole Nationale Supkrieure Pdyfechnique FNSP) de Y a m " , B.P. 8390, Yaoundk, Abstract -The authors present a new method to characterise and discriminate oil slicks and some look-alikes in ERSZ S A R images according only to the observed sea roughness, to reduce oil spill detection and monftoring systems cost. It exploit sea wave spectrum images from the multiscale analysis based on a modified morphological pyramid. Many backscatter characteristics extracted at each level, depended on object and background features are normalized to make its spectral scales be identical. Twenty objects (spot and border) backscatter features have been measured. Eleven sea surface slicks types have been analysed, namely oil, atmospheric instability, wind front, unstable air-mass, current front, falling land wind, large gravity waves, low wind area, natural slicks, swell visible and wind sheltered area, The results presented as smoothed basic profires and texfural specfru allow io tackle oil slicks supervised classification in new images. Oil slicks and currentfront are discriminated. But, some ambiguities of sIicks discrimination in SAFt images remain persistent.

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Multiscale oil slick segmentation with Markov chain model

Cited by 16 publications

References 9 publications

Partially Supervised Oil-Slick Detection by SAR Imagery Using Kernel Expansion

Partially Supervised Oil-Slick Detection by SAR Imagery Using Kernel Expansion

A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis

Sea surface slicks characterization in SAR images

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