Recent Advances in Synthetic Aperture Radar Data Processing and Application
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Gui Gao, Gongtao Shi, Shilin Zhou, "Modeling Multilook Magnitude and Phase in Extremely Heterogeneous Clutter", International Journal of Antennas and Propagation, vol. 2013, Article ID 468380, 5 pages, 2013. https://doi.org/10.1155/2013/468380
Modeling Multilook Magnitude and Phase in Extremely Heterogeneous Clutter
Abstract
This paper proposes a joint distribution of magnitude and phase for multilook SAR interferogram in extremely heterogeneous clutter. The presented theoretical distribution, called simply here distribution and derived from the multiplicative model and the known joint distribution of the homogeneous clutter, is shown to be able to model the extremely heterogeneous clutter areas. Moreover, we estimate distribution parameters by means of the wellknown methodoflogcumulants (MoLC). The experimental results applied on actual dualchannel SAR images prove the good performance of the proposed distribution.
1. Introduction
Ground moving target identification (GMTI) using synthetic aperture radar (SAR) has been a growing interest over the last couple of decades in many applications, such as military surveillance and reconnaissance of ground vehicles and civilian ship monitoring of harbor [1–3]. The recent works [3, 4] reported in this field show that the multilook interferogram, defined as the product of the first channel and the complex conjugate of the second, is an important tool for detecting moving targets. However, precise knowledge of the interferogram’s phase and magnitude statistics, that is, the joint probability density function (PDF), is a major issue currently under study in the development of statistically based detector tests for distinguishing the moving targets from clutter [3–5].
Some investigations for statistical modeling of multilook SAR interferogram have been presented in the past, for example, [3–5]. Lee et al. [5] firstly proposed the joint distribution of interferometric magnitude and phase with the condition of a constant radar cross section (RCS) background based on the complex Wishart distribution, presented by Goodman [6]. The analytical expression of this joint distribution is given as [5] where and are the normalized interferogram’s magnitude and the multilook phase. represents number of looks, and indicates the magnitude of the complex correlation coefficient. is the second type modified Bessel function with order . indicates the Gamma function. In the analysis of lot of literatures, it is shown [3–5] that the PDF shown in (1) is valid for modeling homogeneous areas, whereas it also tends to deviate strongly in most cases whose scenes contain heterogeneous or extremely heterogeneous regions.
Additionally, as the phase statistic is highly invariant against changes of the clutter type [3], the marginal PDFs of interferometric magnitude for heterogeneous and extremely heterogeneous regions are derived by Gierull [4]. Meanwhile, an original joint PDF of interferometric magnitude and phase for heterogeneous clutter is also given in aforementioned literature. Unfortunately, the joint PDFs of interferometric magnitude and phase for extremely heterogeneous regions are still a hard task by means of combining the marginal PDFs of magnitude and the ones of phase owing to that and are not statistically independent [3].
In this paper, our objective is to present a novel joint distribution of interferometric magnitude and phase for extremely heterogeneous clutter. We test the performance of the proposed distribution utilizing a representative dualchannel SAR image of urban area described as an extremely heterogeneous region.
2. The Joint Distribution
2.1. The Known Joint Distribution for Heterogeneous Regions
Considering an look interferogram , it is the average of singlelook interferograms. Assuming the energy of two channels is identical, it is well known that can be modeled by the multiplicative model as [4] where represents the backscattering RCS magnitude of each channel.
As analyzed by Frery et al. [7], the random variable obeys a reciprocal of the square root of a Gamma distribution to characterize highly heterogeneous situation, that is, . Supposing , the PDF of is given by where () is a shape parameter, which essentially reflects the degrees of homogeneity for processed areas. is a scale parameter related to the mean energy of processed areas.
Therefore, the modified interferometric magnitude of heterogeneous clutter is given as and the joint distribution of and can be expressed by
Applying (1), we get the right handside of the integral shown in (4) as
Combining (3) and (5) by (4), and utilizing the integral formula + [8], the joint distribution of magnitude and phase in the heterogeneous clutter is finally derived as [4] where is the Gauss hypergeometric function and
2.2. The New Joint Distribution for Extremely Heterogeneous Regions
For extremely heterogeneous clutter like the urban areas, the histograms show the heavy trail. To solve this problem, Gierull [4] proposed a transformation of into , where the interferometric magnitude and phase of extremely heterogeneous clutter can be derived by (6) with Jacobian to where and are the following functions:
Hereafter (8) is denoted simply by . The marginal PDF of interferometric magnitude is further given by integrating (8) with respect to the phase as
2.3. Relationship between Distributions
The relations of the aforementioned joint distributions are summarized in Figure 1, where the symbol denotes the convergence in distribution. It is clear from the derivations of (5) and (8) that the distribution converges to the when . Similarly, also converges to under the condition that . The properties stated in Figure 1 show that either homogeneous, heterogeneous, or extremely heterogeneous interferogram's magnitude and phase statistics can be treated as the outcome of the proposed distribution.
3. Parameter Estimations
The MoLC, which relies on the Mellin transform [9], is a more feasible parametric PDF estimation technique for distributions defined on . Given as a function defined over , the Mellin transform of is defined as
Thus, the secondkind first characteristic function and the secondkind second characteristic function are given, respectively, by
The estimate of parameter has been obtained by Abdelfattah and Nicolas; the details can be found in [10]. However, here, we are interested in estimates of the parameters , and in the distribution. To make the deriving process be simplified, we notice that the parameters of the distribution and the corresponding marginal PDF of magnitude given by (10) are identical. Motivated by this property, by plugging (10) into (12), one gets
The kthorder derivative of at is the kthorder secondkind cumulant also named “logcumulant.” Consequently, the thorder logcumulants corresponding to the distribution are where represents the digamma function and is the kthorder polygamma function. Additionally, the logcumulants can be directly estimated by samples as follows:
The functions with varying are shown in Figure 2 Thus (15) is continuous and strictly monotonically for each parameter of , , and . To obtain the numerical solution quickly and simply, we set as an even (i.e., letting , and in (15), resp.). On the other hand, we stress that (15) does not contain , thus allowing us to split the nonlinear solution procedure into two distinct stages. First, the estimates , , and of the parameters , , and are accquired by solving (15) and (16), that is,
Second, via (14) and (16), the estimate of the parameter is
4. Experimental Analysis
In this section, we present simulation results obtained by measured SAR data using the proposed distribution. Especially, we provide the fitting performance of urban area indicated as extremely heterogeneous clutter to support the prior theoretical analysis that the distribution is able to model the clutter areas with widely varying degrees of homogeneity.
As a representative example, the test dualchannel SAR data of urban used in this study were acquired by an airborne SAR system of China in Beijing operated in X band and HH polarization, with the spatial resolution 10 m 2 m (azimuth range); see Figure 3. The threedimension histogram of the urban interferogram is shown in Figure 4.
We apply the proposed parametric estimation algorithm in Section 3 to the abovementioned urban area. The results of parametric estimation of the corresponding distribution are listed in Table 1. Based on these estimated parameters, the fitting histogram is given in Figure 5. It is clearly seen that the distribution performs very well in fitting the histogram of the urban area from the viewpoint of the visual comparison between the histogram and the estimated PDF.

Furthermore, in order to assess the effectiveness of the presented distribution, we test the developed model on the previous urban area by limiting various phase values. Figure 6 yields the fitting result with the condition of . It is easy to observe that the distribution agrees well with the given urban area which implies the wide modeling ability of the proposed distribution, as expected.
5. Conclusion
In this paper, we have developed a joint distribution of magnitude and phase for multilook SAR interferogram in extremely heterogeneous clutter. We also provide the corresponding parameter estimation technique based on the MoLC. The theoretical analysis and experimental results of measured multilook SAR data both have shown the good performance modeling extremely heterogeneous clutter. Since either homogeneous, heterogeneous, or extremely heterogeneous interferogram's magnitude and phase statistics can be treated as the outcome of the proposed distribution (as Figure 1 stated), the presented distribution turns out to be suitable for the clutter with widely varying degrees of homogeneity.
Acknowledgment
The author would like to acknowledge the National Natural Science Foundation of China for the support under Grant 41171316.
References
 D. CeruttiMaori, J. Klare, A. R. Brenner, and J. H. G. Ender, “Widearea traffic monitoring with the SAR/GMTI system PAMIR,” IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 10, pp. 3019–3030, 2008. View at: Publisher Site  Google Scholar
 E. Chapin and C. W. Chen, “Alongtrack interferometry for ground moving target indication,” IEEE Aerospace and Electronic Systems Magazine, vol. 23, no. 6, pp. 19–24, 2008. View at: Publisher Site  Google Scholar
 I. C. Sikaneta, Detection of ground moving objects with synthetic aperture radar [Ph.D. thesis], University of Ottawa, 2002.
 C. H. Gierull, “Statistical analysis of multilook SAR interferograms for CFAR detection of ground moving targets,” IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 4, pp. 691–701, 2004. View at: Publisher Site  Google Scholar
 J. S. Lee, K. W. Hoppel, S. A. Mango, and A. R. Miller, “Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 32, no. 5, pp. 1017–1028, 1994. View at: Publisher Site  Google Scholar
 N. R. Goodman, “Statistical analysis based on a certain multivariate complex gaussian distribution (an introduction),” The Annals of Mathematical Statistics, vol. 34, no. 1, pp. 152–177, 1963. View at: Publisher Site  Google Scholar
 A. C. Frery, H. J. Muller, C. C. F. Yanasse, and S. J. S. Sant'Anna, “A model for extremely heterogeneous clutter,” IEEE Transactions on Geoscience and Remote Sensing, vol. 35, no. 3, pp. 648–659, 1997. View at: Publisher Site  Google Scholar
 S. I. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series, and Products, Academic Press, San Diego, Calif, USA, 7th edition, 2007.
 J. M. Nicolas, “Introduction to second kind statistic: application of logmoments and logcumulants to SAR image law analysis,” Traitement du Signal, vol. 19, no. 3, pp. 139–167, 2002. View at: Google Scholar
 R. Abdelfattah and J. M. Nicolas, “Interferometric SAR coherence magnitude estimation using second kind statistics,” IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 7, pp. 1942–1953, 2006. View at: Publisher Site  Google Scholar
Copyright
Copyright © 2013 Gui Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.