SEA CLUTTER STATISTICAL CHARACTERIZATION USING TERRASAR-X DATA Eduardo Makhoul, Yu Zhan, Antoni Broquetas, Josep Ruiz-Rodon, Stefan Baumgartner∗ Signal Theory and Communications Department, Universitat Polit`ecnica de Catalunya (UPC) Campus Nord D3-114, c/Jordi Girona 1-3, 08034, Barcelona, Spain; ∗ German Aerospace Center (DLR), Microwaves and Radar Institute, D-82234 Oberpfaffebhofen, Germany E-mail:
[email protected]; Phone: +34 934017359; Fax: +34 934017232
1. INTRODUCTION Spaceborne synthetic aperture radars (SAR) are potential sensors for globally monitoring maritime activities. On the one hand, the increase in piracy acts, vessel hijacking and the need to control fishing and tanker polluting activities, imposes the necessity to properly monitor maritime traffic. In this sense, SAR sensors provide great capabilities for ship surveillance by means of ground moving target indication (GMTI); some preliminary evaluation has been carried in [1]. On the other hand, alongtrack interferometric (ATI) SAR configurations can provide high resolution images of ocean, sea and river current velocity distributions, which is an important research field in a wide variety of oceanographic and hydrological applications, [2]. Proper optimization of SAR-GMTI missions, as the novel configuration proposed and studied in [3], provides improved detection capabilities when compared to current state-of-the-art missions as TerraSAR-X or TanDEM-X, allowing to detect small and slow moving vessels. In order to properly asses the GMTI performance of any spaceborne SAR-GMTI architecture, realistic modeling of sea clutter returns is required. Several approaches to model the imaging radar response of the sea exist; either based on a more physical (electromagnetic) description [4], or as a stochastic characterization of the interaction between the radar signal and the sea, [5]. This second approach is more attractive, due to the simplicity in its generation as well as from the performance evaluation point of view, as it can be easily integrated in a statistical-like simulation, e.g Monte Carlo trials, and/or in a raw-data based simulation as proposed in [3]. The sea clutter can be modeled as a K-distribution for low grazing angles, [6], based on the compound model theory. However, the validity of such a model should be carefully analyzed, with special interest on the spaceborne SAR case, where not many and exhaustive studies are available to properly validate a common statistical framework. Moreover, it lacks a specific study of the cross-polar channels (HV, VH) over the sea. These considerations motivate the stochastic characterization of sea clutter using spaceborne TerraSAR-X data, in order to analyze, under some specific scenario conditions, the K-distribution fitting of the data. The objective is to extract meaningful information to be used as an input to realistic sea clutter simulations, which are fundamental to properly asses the GMTI capabilities of any SAR-GMTI mission for maritime applications. Thanks to the DLR for the TerraSAR-X Qual-Pol data provided in the frame of the DRA proposal MTH1971 and to the FP7-SPACE Project NEREIDS for the TerraSAR-X Dual-Pol data. This work has been supported by FPU Research Fellowship Program, Ministerio de Educacin, contract AP2009-4590; by FPI-AGAUR Research Fellowship Program, Generalitat de Catalunya, contract 2010FI EM0515757; and by the Spanish Ministry of Science and Innovation (MICINN) under projects TEC2011-28201-C02-01 and CONSOLIDER CSD2008-00068.
RADIOMETRIC CALIBRATION
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Fig. 1. Flow chart for the statistical characterization of the sea clutter using experimental data.
Fig. 2. TerraSAR-X color-composite quicklooks for the Bermuda (left) and Mauritania (right) acquisitions.
2. STATISTICAL ANALYSIS APPROACH In Fig. 1 the flow chart for the statistical characterization of the sea clutter is depicted. The implemented module can accept either single channel or multichannel data. For each channel, and after the corresponding radiometric calibration, a sliding window, with programmable dimensions and overlapping, extract different areas of interest (AOI). In the next stage, the AOI is passed through a simple outlier filter, such that man-made structures with high reflectivity (as vessels, oil platforms, wind farms) are excluded from the analysis, avoiding the corruption of the underlying statistics. The pixels are filtered out if their magnitude x exceeds the mean statistical value µAOI in a given number of times (β) the standard deviation σAOI . The mean and variance of the magnitude are estimated using the whole AOI, whereas the factor β is adjusted experimentally, typical values are between 4 to 6, when big vessels or wind farms are present. Afterwards, the statistical parameters to characterize the different assumed distributions (Rayleigh and K-distribution for magnitude) are estimated. The Kolmogorov-Smirnov test is carried out in order to identify whether the theoretical statistics fit the data. Additionally, the third and fourth order normalized intensity moments (NIM) NIMn =
E{z n } n (E{z})
(1)
are estimated as they have been proven to be useful quantitative tests to evaluate the fitness of the available data with given theoretical distributions, [6]. In (1), E{·} corresponds to the statistical expectation and z refers to the echo intensity or power. 3. EXPERIMENTAL RESULTS The statistical analysis has been carried out over two TerraSAR-X data sets: the first one is a full polarimetric (Quad-Pol) acquisition over the Atlantic ocean close to the islands of Bermuda and the second is a dual polarimetric (Dual-Pol) acquisition close to the coastal zone of Mauritania. In Fig. 2 the corresponding composite color coded images are represented. For the Mauritania case, only the homogeneous upper part of the image has been considered for statistical characterization. A sliding window of 512x512 pixels with an overlapping of 128 pixels in both range and azimuth has been used. From the probability density function (PDF) fitting of the HH-magnitude in Fig. 3 (a) for a single AOI over Mauritania’s coast, the K-distribution shows good agreement in the whole dynamic range, specially in the tail’s distribution. In Fig. 3 (b) the estimated mean shape parameter v for the different AOIs is represented as a function of the incidence angle for the different
polarimetric channels and acquisitions. In the cross-polar channels the mean shape parameter significantly varies between 100 and 1000, which means a Rayleigh-like distribution. For the co-polar channels, v is around 7-10 for the Bermuda’s acquisition, whereas for the Mauritania’s case these values are close to 3, in accordance to the more structured returns observed in the image over the Mauritania’s coast, see Fig. 2. A good fit of the real data with the K-distribution is demonstrated also in Fig. 3 (c), where the third and fourth NIM of the HH polarization for both acquisitions are represented and compared to the theoretical ones (solid lines). In Fig. 3 (d) the estimated mean normalized reflectivity σ 0 is represented for the different AOIs as a function of the incidence angle. The cross-polar channels have similar trends as the the noise-equivalent sigma zero (NESZ), in solid lines, being the Bermuda’s case closer due to the widening of the receiving antenna pattern and the reduction of the effective pulse repetition frequency (PRF). From the available ground truth (http://www.ndbc.noaa.gov/) for the Bermuda acquisition, the wind speed is 6.5 m/s (20 minutes after the acquisition), such that the semi-empirical NRL model [7] predicts values of σ 0 between -15.3 and -14.3 for HH and between -16.4 and -15.9 for VV, considering an incidence angle variation of 29.6-31.8 degrees. It must be noted that the NRL model presents an absolute deviation around 3 dB with respect to the reference tables of Nathanson [8]. A statistical approach is used to generate a K-distributed sea clutter in the SAR image domain based on the compound model: the 2-dimensional (2D) spatial correlation, extracted from the SAR image by using sea spectrum inversion techniques, is applied over the Gaussian processes in charge of generating the Gamma distributed process that modules the mean power of the rapidly varying speckle process. Taking into account the estimated shape parameters, the mean σ 0 and the 2D sea SAR spectrum for the Mauritania’s case (and for a given AOI), the generated SAR sea clutter is shown in Fig. 3 (f). Similar structures are observed when compared to the original image patch in 3 (e). The main differences can be related to the accuracy of the sea spectrum extraction and the random nature of the generation process. A finer refinement will be presented in the final paper version. 4. SUMMARY A statistical characterization of the sea clutter using polarimetric TerraSAR-X data is considered in this paper. Two different data takes, close to the Bermuda’s Islands (Quad-Pol) and to the coast of Mauritania (Dual-Pol), are analyzed. It is shown that the K-distribution properly fits the magnitude statistics. This statistical parametrization is used to feed a SAR sea clutter image generator, emulating realistic maritime clutter scenarios. 5. REFERENCES [1] E. Makhoul, A. Broquetas, and J. Ruiz Rodon, “Ground moving target indication using multi-channel SAR with nonuniform displaced phase centers,” in Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, 2012, pp. 1610–1613. [2] R. Romeiser, S. Suchandt, H. Runge, U. Steinbrecher, and S. Grunler, “First Analysis of TerraSAR-X Along-Track InSARDerived Current Fields,” IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 2, pp. 820–829, 2010. [3] E. Makhoul, A. Broquetas, J. Ruiz-Rodon, Y. Zhan, and F. Ceba, “A Performance Evaluation of SAR-GMTI Missions for Maritime Applications,” IEEE Transactions on Geoscience and Remote Sensing, 2013, submitted for publication. [4] D.R. Lyzenga, “Numerical Simulation of Synthetic Aperture Radar Image Spectra for Ocean Waves,” IEEE Transactions on Geoscience and Remote Sensing, vol. GE-24, no. 6, pp. 863–872, 1986. [5] P.W. Vachon, R.K. Raney, and W.J. Emergy, “A simulation for spaceborne SAR imagery of a distributed, moving scene,” IEEE Transactions on Geoscience and Remote Sensing, vol. 27, no. 1, pp. 67–78, 1989.
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Fig. 3. Sea clutter characterization results (a)-(d); generated sea clutter SAR image (f). [6] K. D. Ward, R. J.A. Tough, and S. Watts, Sea Clutter: Scattering, the K Distribution and Radar Performance, The Institution of Engineering and Technology, 2006. [7] V. Gregers-Hansen and R. Mital, “An improved empirical model for radar sea clutter reflectivity,” IEEE Transactions on Aerospace and Electronic Systems, vol. 48, no. 4, pp. 3512–3524, 2012. [8] Fred E. Nathanson, J. Patrick Reilly, and Marvin N. Cohen, Radar Design Principles: Signal Processing and the Environment, McGraw-Hill, 2nd edition, 1990.
