Unsupervised Wishart Classifications of Sea-Ice using Entropy, Alpha and Anisotropy decompositions

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1 Unsupervised Wishart Classifications of Sea-Ice using Entropy, Alpha and Anisotropy decompositions A. Rodrigues (1), D. Corr (1), K. Partington (2), E. Pottier (3), L. Ferro-Famil (3) (1) QinetiQ Ltd, Room 1058, A8 Building, QinetiQ, Cody Technology Park, Farnborough, Hants. UK. GU14 0LX, (2) Vexcel UK, Arrowfield House, 6 Pound Street, Newbury, Berkshire. RG14 6AA, UK kim.partington@vexcel.co.uk (3) IETR, Université de Rennes I, Bât 11D, 263 Avenue Général Leclerc, CS 74205, Rennes Cedex, France eric.pottier@univ-rennes1.fr ABSTRACT Sea-ice classification based on scattering mechanisms is investigated using the Unsupervised Wishart Classification with Entropy, Alpha and Anisotropy decomposition parameters. L and C Band JPL AIRSAR fully polarimetric data from the Beaufort Sea during winter/freeze conditions are used for testing the algorithm. Initial results show that, within specified ranges of incidence angles, there is good discrimination between the major ice classes, with L-Band giving better first year ice discrimination than C band. 1 INTRODUCTION Sea ice has proved to be one of the most mature operational applications for satellite synthetic aperture radar (SAR) data. Ice centres across the northern hemisphere make use of SAR data in preference to all other satellite data [1] to prepare their ice charts. There are also several examples of the data being used to support individual vessels in their tactical navigation, by direct broadcast to the vessel [2]. Despite the maturing of SAR as a dataset for sea ice applications, there are limitations that restrict the market for the data, both among scientists and operational entities. A problem is the attempt to derive information on complex and diverse surfaces from a fleeting glance at a pre-defined polarisation, wavelength and incidence angle. There have been a very large number of land-based studies that have shown that polarimetry gives unique information about the structure of surfaces. It is therefore of interest to extrapolate this knowledge to the discrimination of different types of ice surface. A review of recent literature has shown that the use of multiple polarizations is beneficial for Sea Ice Classification. In some cases the quantifiable benefit is difficult to assess. This is because the classical polarimetric parameters of co-polarisation ratio, correlation coefficient and co-polar phase have broad distributions that overlap between the major ice types [3]. At C band, polarimetric signatures have higher variability than at L-band [3]. C band shows evidence of volume scattering in old ice, with polarimetric parameters reflecting this, while some thin and FY ice types show evidence of Bragg scattering with polarimetric parameters reflecting this too. Thin ice also shows evidence of some influence of brine inclusions at C band and the ice-water interface at L band, the latter influencing the copolar phase [4]. The evidence to date is that L band is likely to be more valuable for polarimetric information than C band. A promising technique for classifying sea ice is considered to be the target decomposition technique of Cloude and Pottier [5] together with the Wishart classifier [6]. This makes explicit use of the different scattering mechanisms that are known to be associated with different types of sea ice. It provides a more theoretical basis on which to classify sea ice than has hitherto been available. In this paper we apply the Wishart classifier to polarimetric SAR data of the Beaufort Sea. The classifications are unsupervised they are seeded from entropy (H), alpha (α) and anisotropy (A) target decomposition parameters.

2 2 SAR IMAGE AND GROUND TRUTH DATA L and C Band JPL AIRSAR data of the Beaufort Sea was used in the study. The data was acquired in 1989 and is fully polarimetric. P Band data was also available, but this was not used for classification. The SAR data was supplied calibrated. The Copolar phase calibration was checked using the multi-year ice floes. The Copolar phase was found to be confined to 0 O +/- 3 o for these areas Figure 1 shows C,L,P multi-frequency colour composites of the two AIRSAR scenes that are discussed in this paper. range Figure 1 - C, L, P band multi-frequency colour composites of Beaufort Sea. 183 dataset (left) and 1372 dataset (right) The colours in Figure 1 enable a visual classification of the scenes. The red/brown areas are Multi-Year ice, with the exception of the streaked area in the top left of dataset 183 this is a form of thin ice, possibly with frost flowers. Light blue / white lines are ridged first year ice. Newly forming ice appears as near-black. Areas of compressed ice between the multi-year ice floes appear as white. Blue areas are smooth compressed first year ice. These visual interpretations were used to assess the classifications, in association with other environmental data that was available from nearby monitoring stations [6] 3 SOFTWARE DEVELOPMENT AND DATA PROCESSING To perform the classification several software modules were developed. These included: Data import and export for DLR E-SAR, JPL AIRSAR data Polarimetric speckle reduction using edge enhanced windows Entropy, alpha, anisotropy Polarimetric decomposition Wishart Classification - automatic seeding from H/α and H/α/A space (Unsupervised classification) The data processing chain is shown in Figure 2. For the results that are shown in this paper, a 7 by 7 speckle filtering window was used. Four classifications were performed for each of the scenes shown in Figure 1. More information on the Unsupervised classification process and seeding using polarimetric decomposition parameters can be found in [7]. The classifications are: C Band Unsupervised Wishart classification with H/α seeding C Band Unsupervised Wishart classification with H/α/A seeding L Band Unsupervised Wishart classification with H/α seeding L Band Unsupervised Wishart classification with H/α/A seeding

3 Polarimetric SAR data Polarimetric speckle reduction Polarimetric decomposition Unsupervised classification Classification Eliminate pixels with poor S/N Visual comparison of results with ground truth Figure 2 Data processing chain In order to take account of pixels that have poor signal to noise values, a mask was applied after classification. There were no areas where the signal to noise ratio (S/N) was consistently low. Results In this section the unsupervised classifications are shown. The colour that has been assigned to each class is determined from a linear hue scale. Figure 3 shows the C-Band unsupervised classifications. H-a -seeded Wishart classifications H-a-A-seeded Wishart classifications Figure 3 - C-Band Unsupervised classifications 183 dataset (left), 1372 dataset (right)

4 The C Band H/α-seeded classification has discriminated multi-year ice from first year ice. However, it has not discriminated well between the different types of first year ice. Most of the first year ice has been merged with newly forming ice. The C Band H/α/A-seeded classification performs as well as the H/α-seeded classification for old and first year ice discrimination, but also is able to discriminate ridged first year ice from other types of first year ice. There is also some evidence of increased discrimination of smooth first year ice in the 1372 dataset. All of the C Band classifications suffer incidence angle effects. In these images the incidence angle varies from approx. 27 degrees near range to approx 53 degrees far range. The classifications exhibit a banding of colours in the direction of increasing range in the image. This is likely to be due to the change in surface scattering as a function of incidence angle. This change in surface scattering with increasing incidence angle is also manifest as a change in amplitude this can be seen in Figure 1. This is a reflection of the physical nature of the decomposition technique. As the incidence angle changes, so the balance between different scattering mechanisms, and the mechanisms themselves, change. The technique therefore is sensitive to this by definition. Figure 4 shows the unsupervised classifications at L Band. H-a-seeded Wishart classifications H-a-A-seeded Wishart classifications Figure 4 - L-Band unsupervised classifications, 183 dataset (left), 1372 dataset (right) With the 183 dataset, the L Band H-α seeded classification discriminates well between multi-year and all of the first year ice. However, in the 1372 dataset the first year ridged ice has been confused with the multi-year ice. The H-alpha seeded classification also does not discriminate compressed ice from smoothed ice. With L Band H-α-A seeded classification, there is good discrimination between all the major ice types. The unsupervised classification does contain many more ice types than the 5 major ice types within the scene, but it is possible to manually merge the classes to achieve a mapping that is closer to unity. For example merging the red and cyan classes leads to a class that contains all of the newly forming ice. Also, if the yellow and mustard classes are merged than the new class contains the majority of the Multi-year ice. It is thought that the L band classifications are better for two main reasons. For each ice class, the distribution of polarimetric parameters is narrower at L band than at C band

5 L band is known to be more sensitive to surface roughness than C band [3]. A characteristic of First Year ice is a wide range of surface roughness. The L Band classifications are less sensitive to incidence angle effects. This sensitivity has not been eliminated the large multi-year icefloes in Figure 4 (top left) are purple at the top (near range) of the image but are yellow near the bottom (far range) of the image. However, the difference in incidence angle between these areas is approximately 20 o, much larger than would be case with the planned polarimetric spaceborne SAR sensors. Table 1 summarises the accuracy of the classifications at each frequency and with both types of seeding. Dataset 183 Frequency & seeding method Discriminate MY year from FY ice? Discriminate NF from CI/RI? Discriminate CI from RI? C Band, H, α Yes No No C Band H, α, A Yes No No L Band H, α Yes Partial No L-Band H, α, A Yes Yes Yes Dataset 1372 Frequency & Discriminate MY year Discriminate MY/NF from Discriminate CI from RI? seeding method from NF and FY ice? CI/RI? C Band, H, α Yes No No C Band H, α, A Yes Partial Partial L Band, H, α Yes Partial No L-Band, H, α, A Yes Yes Partial MY Multi-year ice, FY=First Year ice, NF= Newly formed ice, CI=compressed ice, RI=ridged ice Class merging Table 1 Summary of ice discrimination for 183 and 1372 datasets The results shown in this paper have demonstrated that good results are obtained when fully polarimetric L Band data is classified using H/α/A-seeded unsupervised classification, based on Wishart statistics. Nevertheless, some user input is required to assign the radar-derived classes to genuine ice classes. It can be seen that many ice classes are actually split into several radar-derived classes. Lee et al proposed a metric to compute the inter-cluster distance in [7]. This metric was used to calculate the inter-cluster distances for dataset 183 A classification of the L-Band 183 image leads to 14 classes. In Table 2, the inter-cluster distances are shown Table 2 - Inter-cluster distance measures

6 It was hoped that the data in Table 2 could be used to develop a simple class-merging scheme that may aid the user when assigning the classes post classification. However, this was found not to be the case. Inspection of Table 2 shows that in some cases: The distance of a cluster to itself (diagonal elements) is not zero Sometimes the distance of a cluster to itself is greater than the distance from that cluster to another cluster (an example is shown in Table 2 with red numbers there are many more examples in the table) These observations (particularly the second) mean that it is not easy to merge the classes automatically. If the distance of a cluster to itself is greater than the distance to another cluster, it is not apparent how to devise a simple linear criterion to merge classes that are adjacent to one another. Summary Unsupervised classification using Wishart statistics was performed on JPL AIRSAR L and C band fully polarimetric SAR data of the Beaufort Sea. At L band with entropy, alpha and anisotropy seeding there was good discrimination between all the major ice types. C Band was able to discriminate between multi-year and first year ice but was unsuccessful at discriminating between the different types of first year ice in the scene. L Band was much less sensitive to incidence angle effects than C Band. An attempt to automatically merge the L band classes post classification was not successful because the intercluster distance metric was found to have some non-linear properties. It is recommended that further work on class merging is performed. Acknowledgements The funding for this work was kindly provided by ESA ESRIN. The AIRSAR data used in this study was kindly provided by the Jet Propulsion Laboratories, NASA. The authors would like to thanks Eric Rignot, Ben Holt and Mark Drinkwater for assistance in obtaining the AIRSAR data. References 1. Bertoia C., D. Gineris, K. Partington, L.-K. Soh and C. Tsatsoulis, 1999, Transition From Research to Operations: ARKTOS: A Knowledge-Based Sea Ice Classification System, IGARSS'99, Hamburg, Germany. 2. Partington, K.C. and 7 co-authors, 1994, "A demonstration system for monitoring sea ice from space in "Marine, Offshore and Ice Technology" (eds T. Murphy, P. Wilson and P. Wadhams), publ Computational Mechanics Publications, Drinkwater, M., Kwok, R., Winebrenner, D., Rignot, E., 1991, Multifrequency polarimetric synthetic aperture radar observations of sea ice, Jnl. Geophs. Res., vol. 96, no. C11, pp. 20,679-20, Winebrenner, D.P., L.D. Farmer, and I.R. Joughin, "On the response of polarimetric SAR signatures at 24-cm wavelength to sea ice thickness in Arctic leads," Radio Science 30(2), pp , Cloude, S. and Pottier, E, 1996, A review of target decomposition theorems in radar polarimetry, IEEE Trans. Geosci. Rem. Sens., Vol. 34, No. 2, pp Rignot, E. and Drinkwater, M., 1994, Winter sea ice mapping from multi-parameter synthetic aperture radar, Jnl. Glac., vol. 40, no. 134, pp Lee, J.S, Grunes, M.R., Ainsworth, T.L., Schuler, L.J. and Cloude, S.R., Unsupervised Classification using Polarimetric Decomposition and the Complex Wishart Distribution, IEEE Trans Geosci. Rem Sens., Vol 37/1 No5, pp2249 ª Copyright QinetiQ Ltd 2003