SAR polarimetry for classification of sea ice: a comparison of physical based algorithms on ICESAR data

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Introduction: The observation of sea ice is a major topic in remote sensing due to the difficulty of performing frequent in situ expeditions [1, 2]. Monitoring of sea ice is important for many environmental issues [1]. First of all, it is a sensitive climate indicator and it plays an important role in global climate systems. It restricts the exchange of heat and chemical constituents between ocean and atmosphere acting as an insulator. Moreover, it influences global climate system for effects related with its elevated albedo, reducing the amount of solar radiation absorbed at the Earth’s surface. On the other hand, sea ice affects oceanic circulation directly by the rejection of salt to the underlying ocean during ice growth, that is responsible for deep water formation. Besides these, the possibility and safety of navigation in Polar Regions is severely influenced by the presence of sea ice. SAR: Microwave sensors and Synthetic Aperture Radar (SAR) are very valuable for monitoring of sea ice since they can acquire information in absence of solar illumination (i.e. during Polar nights) and with almost any weather conditions. Unfortunately, the description of the backscattering behaviour of sea ice is particularly challenging. For this reason, many scientists moved toward systems able to increase the amount of information acquired. In this context, polarimetry plays a key role, because it is able to enhance the discrimination capability of the observed target, solving many ambiguities revealed in single polarisation images [3]. Specifically, sea-ice could be modelled as a layered media showing several interfaces: air-snow, snow-ice and (eventually) ice-water [4, 2]. SAR polarimetry: A scattering (Sinclair) matrix [S] can be used to characterise the polarimetric behaviour of deterministic targets [3]. A scattering vector k can be obtained rearranging the elements of the scattering matrix. The Pauli basis was widely exploited to rearrange the scattering matrix in what is defined as the Pauli scattering vector k = [HH +VV, HH-VV, 2HV], where H stands for linear horizontal and V for linear vertical and the repeated letter is for transmitter-receiver. A target that pixel per pixel changes its polarimetric behaviour (i.e. scattering matrix) is defined ”partial” and can be characterised with the second order statistics of the scattering vector. The latter are generally arranged in a covariance matrix [C]. Sea-Ice classification with SAR polarimetry: This paper will compare three different classification methodologies that make advantage of polarimetric SAR data. 1) The first considers the estimation of “polarimetric observables” (ratios and coherences between polarimetric channels) to build a feature vector able to separate the different ice types (and open water) on a multidimensional space. This approach was largely adopted in the literature and its value is a consequence of the choice of observables that physically should capture the different behaviour of ice types and open water [2,5,6]. 2) The Wishart classifier for the covariance matrix [C]. This approach is based on the statistical distance (in the covariance matrix space) of the pixel from the different classes. The supervised version makes use of a first step where a Cloude-Pottier decomposition is performed [3]. The latter was already exploited in some occasions for sea ice classification [7]. 3) The classifier based on the perturbation analysis [8]. This recent classifier will be tested here since in some conditions showed improvements on the Wishart classifier. Specifically, the overall intensity of the backscattering is neglected and this is beneficial in situations where a modulation of the intensity may not be related to physical but rather geometrical phenomena. The three methodologies will be carefully compared in order to understand which the best methodology for the different situations is. Dataset used: The dataset exploited in this study was acquired during the ICESAR campaign in 2007 by the E-SAR airborne system of DLR (German Aerospace Agency). The sea ice acquisitions were carried out in Svalbard over three different locations: Fram Strait, Storfjord and Barents Sea. In this analysis only L-band acquisitions are used, since they are the only one presenting quad-polarimetric data. The resolution of the system is 2.12m in slant-range and about 1m in azimuth with a pixels spacing of 1.5m in range and 0.5m in azimuth. The NESZ goes from -30dB to -35dB, while the incidence angle from 26 to 65 degrees. Aerial photographs of the area during the acquisition show that first year ice is present, with areas of brash ice. Few small leads are also visible. Discussion: As a final remark, the results that will be presented show that polarimetry could help the data analysis solving eventual ambiguities. However, in many instances, the refrain in exploiting polarimetric modes is the impossibility to achieve very large swath (as ScanSAR images) that in many sea-ice applications are needed to cover vast areas in short time. Fortunately, in the next generations of SAR satellites this inconvenient may be bypassed by the possibility to use compact polarimetry (as for the RADARSAT constellation) or dual polarimetry (as for the Sentinel constellation) with ScanSAR modes. In particular, compact polarimetry somehow allows reconstructing quad-polarimetric data, although part of the information will be clearly lost. References: [1] S. Sandven, Johannessen, O.M., and K Kloster, Sea Ice Monitoring by Remote Sensing, John Wiley & Sons, 2006. [2] C.R. Jackson and J.R. Apel, Synthetic Aperture Radar Marine User’s Manual: National Oceanic and Athmospheric Administration (NOAA), NOAA, 2004. [3] S. R. Cloude, Polarisation: Applications in Remote Sensing, Oxford University Press, 2009. [4] S.V. Nghiem, R. Kwok, S.H. Yueh, and M.R. Drinkwater, “Polarimetric signatures of sea ice 1. theoretical model,” Journal of Geophysical Research, vol. 100(13), pp. 665–679, 1995. [5] Drinkwater, M., Kwok, R., Rignot, R., Israelsson, H., Onstott, R.G., & Winebrenner, D.P. (1992). Chapter 24. Potential Applications of Polarimetry to the Classification of Sea Ice. Geophysical Monograph Series, 68, 419 – 430. [6] H. Wakabayashi, T. Matsuoka, K. Nakamura, and F. Nishio, “Polarimetric characteristics of sea ice in the sea of Okhotsk observed by airborne L-Band SAR,” IEEE Transaction on Geoscience and Remote Sensing, vol. 42, 2004. [7] Scheuchl, B., Hajnsek, I., & Cumming, I.G. (2003). Classification Strategies for Polarimetric SAR Sea Ice Data. Proceedings on POLinSAR. [8] Armando Marino, Shane R. Cloude & Iain H. Woodhouse (2012), Detecting Depolarizing Targets using a New Geometrical Perturbation Filter, IEEE Transaction on Geoscience and Remote Sensing, 50(10), 2012.

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ESA Living Planet Symposium, 09 Sep 2013 - 13 Sep 2013, Edinburgh, Scotland.
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Marino, A. , Dierking, W. , Hajnsek, I. and Wesche, C. (2013): SAR polarimetry for classification of sea ice: a comparison of physical based algorithms on ICESAR data , ESA Living Planet Symposium, Edinburgh, Scotland, 9 September 2013 - 13 September 2013 .

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