Coherence estimation in synthetic aperture radar data based on speckle noise modeling

l'IETR - Institut d’Électronique et de Télécommunications de Rennes, Université de Rennes 1, Roazhon, Brittany, France
Applied Optics (Impact Factor: 1.78). 03/2007; 46(4):544-58. DOI: 10.1364/AO.46.000544
Source: PubMed


In the past we proposed a multidimensional speckle noise model to which we now include systematic phase variation effects. This extension makes it possible to define what is believed to be a novel coherence model able to identify the different sources of bias when coherence is estimated on multidimensional synthetic radar aperture (SAR) data. On the one hand, low coherence biases are basically due to the complex additive speckle noise component of the Hermitian product of two SAR images. On the other hand, the availability of the coherence model permits us to quantify the bias due to topography when multilook filtering is considered, permitting us to establish the conditions upon which information may be estimated independently of topography. Based on the coherence model, two coherence estimation approaches, aiming to reduce the different biases, are proposed. Results with simulated and experimental polarimetric and interferometric SAR data illustrate and validate both, the coherence model and the coherence estimation algorithms.

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Available from: Carlos López-Martínez,
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    • "A more thorough analysis shows that the accuracy of coherence estimation is sensitive for all error sources with different degrees, according to underlying decorrelation effects, sample sizes, topography and the types of land cover, etc. The combination of two or more algorithms to accurate coherence estimation is always suggested if we can use coherence observations quantitatively [15], [17], [25], [26]. These algorithms, however, usually being interactional , may yield an undesired effect on final coherence values. "
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    ABSTRACT: The coherence of radar echoes is a fundamental observable in interferometric synthetic aperture radar (InSAR) measurements. It provides a quantitative measure of the scattering properties of imaged surfaces and therefore is widely applied to study the physical processes of the Earth. However, unfortunately, the estimated coherence values are often biased due to various reasons such as radar signal nonstationarity and the bias in the estimators used. In this paper, we focus on multitemporal InSAR coherence estimation and present a hybrid approach that mitigates effectively the errors in the estimation. The proposed approach is almost completely self-adaptive and workable for both Gaussian and non-Gaussian SAR scenes. Moreover, the bias of the sample coherence can be mitigated with even only several samples included for a given pixel. Therefore, it is a more pragmatic method for accurate coherence estimation and can be applied actually. Different data sets are used to test the proposed method and demonstrate its advantages.
    IEEE Transactions on Geoscience and Remote Sensing 04/2014; 52(5). DOI:10.1109/TGRS.2013.2261996 · 3.51 Impact Factor
    • "However, if we consider spectral coherence at constant gap such that no overlapping occurs between sub-bands, spectral coherence values do not fall completely to zero as expected and stays around 0.1. This is due to the fact that coherence is not measured but estimated, like in classical interferometry when estimating temporal coherence, and the used estimator is known to be biased at low coherence level [13]. "
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    ABSTRACT: Multi-Chromatic Analysis (MCA) of SAR images relays on exploring sub-band images obtained by processing portions of range spectrum located at different frequency positions. It has been applied to interferometric pairs for phase uwrapping and height computation. This work investigates two promising applications: the comparison between the frequency-persistent scatterers (PSfd) and the temporal-persistent scatterers (PS), and the use of inter-band coherence of a single SAR image for vessel detection. The MCA technique introduces the concept of frequency-stable targets, i.e. objects exhibiting stable radar returns across the frequency domain which is complementary to that of temporal stability at the base of PS interferometry. Both spotlight and stripmap TerraSAR-X images acquired on the Venice Lagoon have been processed to identify PSfd and PS. Different populations have been analyzed to evaluate the respective characteristics and the physical nature of PSfd and PS. Concerning the spectral coherence, it is derived by computing the coherence between sub-images of a single SAR acquisition. In the presence of a random distribution of surface scatterers, spectral coherence must be proportional to sub-band intersection of sub-images. This model is fully verified when observing measured spectral coherence on open see areas. If scatterers distribution departs from this distribution, as for manmade structures, spectral coherence is preserved. We investigated the spectral coherence to perform vessel detection on sea background by using spotlight images acquired on Venice Lagoon. Sea background tends to lead to very low spectral coherence while this latter is preserved on the targeted vessels, even for very small ones. A first analysis shows that all vessels observable in intensity images are easily detected in the spectral coherence images which can be used as a complementary information channel to constrain vessel detection.
    Proceedings of SPIE - The International Society for Optical Engineering 10/2013; 8891:04-. DOI:10.1117/12.2028962 · 0.20 Impact Factor
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    • "al. demonstrated that the interferometric phase noise can be modeled as an additive noise [3]. Other algorithm is based on a new interferometric phase noise model in the complex plane [2]. Some estimate the InSAR phase within a local estimation window based on the InSAR sample statistics [4] [5]. "
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    ABSTRACT: An interferogram filtering is presented in this paper. The main concern of the proposed scheme is to lower the residues count mean while preserving the location and jump height of the lines of phase discontinuity. The proposed method is based on a statistical model of the coefficients of multi-scale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vector and a hidden positive scalar multiplier. Under this model, the Bayesian least squares estimate of each coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable. The performance of this method substantially has the advantages of reducing number of residuals without affecting line of height discontinuity.
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