Nikola Besic

Polarimetric Incoherent Target Decomposition by Means of Independent Component Analysis

This paper presents an alternative approach for polarimetric incoherent target decomposition (ICTD) dedicated to the analysis of very high-resolution polarimetric synthetic aperture radar (POLSAR) images. Given the non-Gaussian nature of the heterogeneous POLSAR clutter due to the increase in spatial resolution, the conventional methods based on the eigenvector target decomposition can ensure uncorrelation of the derived backscattering components at most. By introducing the independent component analysis (ICA) in lieu of the eigenvector decomposition, our method is rather deriving statistically independent components. The adopted algorithm, i.e., FastICA, uses the non-Gaussianity of the components as the criterion for their independence. Considering the eigenvector decomposition as being analogs to the principal component analysis (PCA), we propose the generalization of the ICTD methods to the level of the blind source separation (BSS) techniques (comprising both PCA and ICA). The proposed method preserves the invariance properties of the conventional ones, appearing to be robust both with respect to the rotation around the line of sight and to the change of the polarization basis. The efficiency of the method is demonstrated comparatively using POLSAR RAMSES X-band and ALOS L-band data sets. The main differences with respect to the conventional methods are mostly found in the behavior of the second most dominant component, which is not necessarily orthogonal to the first one. The potential of retrieving nonorthogonal mechanisms is moreover demonstrated using synthetic data. On the expense of a negligible entropy increase, the proposed method is capable of retrieving the edge diffraction of an elementary trihedral by recognizing dipole as the second component.

Dry snow backscattering sensitivity on density change for SWE estimation

This paper deals particularly with the sensitivity of the wet snow backscattering coefficient on density change. The presented backscattering model is based on the approach used in the dry snow analysis [1], appropriately modified to account for the increased dielectric contrast caused by liquid water presence. It encircles our undertaking of simulating and analysing snow backscattering using fundamental scattering theories (IEM-B, QCA, QCA-CP). The wet snow parameters are chosen according to the area of the particular interest - the French Alps, while the choice of the SAR sensor parameters (frequency, polarization) is primarily conditioned by the initially settled goal - reaching qualitative conclusions concerning wet snow backscattering mechanism. Based on simulation results, we state the dominance of the snow pack surface backscattering component, causing the backscattering to be directly proportional to the volumetric liquid water content. This result is confirmed by the performed in situ measurements. We illustrate as well the decrease of this effect with the increase in operating frequency.

Stochastic Approach in Wet Snow Detection Using Multitemporal SAR Data

This letter introduces an alternative strategy for wet snow detection using multitemporal synthetic aperture radar (SAR) data. The proposed change detection method is primarily based on the comparison between two X-band SAR images acquired during the accumulation (winter) and melting (spring) seasons, in the French Alps. The new decision criterion relies on the local intensity statistics of the SAR images by considering the backscattering ratio as a stochastic process: the probability that “the intensity ratio fits into the predetermined range of values” is larger than a defined confidence level. Both the conducted snow backscattering simulations and the state-of-the-art measurements indicate more complex relation between the backscattering properties of the two snow types, with respect to the conventional assumption of the augmented electromagnetic absorption associated to the wet snow. Therefore, rather than adopting the standard hypothesis, we analyze the wet/dry snow backscattering ratio as a function of the local incidence angle (LIA). After employing the multilayer snow backscattering simulator, calibrated with scatterometer measurements in C-band, we modify, to some extent, the range of ratio values indicating the presence of the wet snow, by including positive ratio values for lower LIA. By simultaneously accounting for the speckle noise, the proposed stochastic approach derives the refined wet snow probability map. The performance analyses are carried out both through the comparison with the ground air temperature map and by comparing two copolarized channels processed separately.

Dry snow analysis in alpine regions using RADARSAT-2 full polarimetry data. Comparison with in situ measurements

In this paper we describe the benefits of RADARSAT-2 in the analysis of temporal changes in polarimetric parameters linked to the snow cover evolution during the winter season. The presented study took place over an instrumented area in the region of French Alps. The focus is set on the dry snow depth retrieval, using an original method based on principal component statistical analysis (PCA) of the polarimetric parameters values. The results obtained by this mean are compared with the network of simultaneous snow in situ measurements. The most thought-provoking result is the strong inverse correlation between the snow depth above the ice crust and the entropy, reflected through the very high coefficient of determination R2 = 0.8439. In order to justify this observation, we propose an appropriate physical hypothesis.

Independent Component Analysis within polarimetric incoherent target decomposition

This paper represents a part of our efforts to generalize polarimetric incoherent target decomposition to the level of BSS techniques by introducing the ICA method instead of the conventional eigenvector decomposition. We compare, in the frame of polarimetric incoherent target decomposition, several criteria for the estimation of complex independent components [1, 2]. This is done by parametrising the obtained dominant and mutually independent target vectors using the TSVM [3] and representing them on the corresponding Poincaré sphere. We demonstrate notably good performances of the proposed method applied on the RAMSES POLSAR X-band image, by precisely identifying the class of trihedral reflectors present in the scene. Logarithm and square root nonlinearities - two of the three proposed criteria for complex IC derivation prove to be very efficient. The best discrimination between the a priori defined classes appears to be achieved with the principal kurtosis criterion. Finally, the algorithm using the former two functions leads to very interesting entropy estimation.

Zernike ultrasonic tomography for fluid velocity imaging based on pipeline intrusive time-of-flight measurements

In this paper, we propose a novel ultrasonic tomography method for pipeline flow field imaging, based on the Zernike polynomial series. Having intrusive multipath time-offlight ultrasonic measurements (difference in flight time and speed of ultrasound) at the input, we provide at the output tomograms of the fluid velocity components (axial, radial, and orthoradial velocity). Principally, by representing these velocities as Zernike polynomial series, we reduce the tomography problem to an ill-posed problem of finding the coefficients of the series, relying on the acquired ultrasonic measurements. Thereupon, this problem is treated by applying and comparing Tikhonov regularization and quadratically constrained ℓ1 minimization. To enhance the comparative analysis, we additionally introduce sparsity, by employing SVD-based filtering in selecting Zernike polynomials which are to be included in the series. The first approach-Tikhonov regularization without filtering, is used because it is the most suitable method. The performances are quantitatively tested by considering a residual norm and by estimating the flow using the axial velocity tomogram. Finally, the obtained results show the relative residual norm and the error in flow estimation, respectively, ~0.3% and ~1.6% for the less turbulent flow and ~0.5% and ~1.8% for the turbulent flow. Additionally, a qualitative validation is performed by proximate matching of the derived tomograms with a flow physical model.

Stochastically based wet snow mapping with SAR DATA

This paper proposes the new method for wet snow mapping using SAR data. It represents a modified version of the existing Naglers mapping method, based on winter/summer image comparison, which is considered as the classic one. Instead of the existing unique threshold, a variable threshold matrix (function of the local incidence angle for each pixel) is proposed, based on dry and wet snow backscattering simulation results. The new membership decision method (with the respect to the dry/snow classes) is introduced. It considers the intensity ratio as a stochastical process: the probability that “the intensity ratio is smaller than the corresponding dry/wet snow determined threshold” is larger than the desired confidence level.

Calibration of a distributed SWE model using MODIS snow cover maps and in situ measurements

In this letter, we propose the calibration procedure for a Snow Water Equivalent (SWE) forecasting model, using Moderate-Resolution Imaging Spectroradiometer (MODIS) multi-temporal snow cover maps and in situ measurements. The presented study refers to one of the largest artificial lakes in the Western Europe – the Serre-Ponçon reservoir, on the Durance river, in the region of the French Alps. The SWE model, an integral part of the MORDOR (MOdèle à Réservoirs de Détermination Objective du Ruissellement) hydrological model, provides SWE as a function of local precipitation and temperature, as well as of accumulation and melting correction coefficients. The principal motivation for the proposed calibration method comes from the significant model sensitivity with respect to these two coefficients, which, given that they account for the influences of topology and mountain winds, ought to vary spatially. Three different optimization procedures are compared using the set of in situ measurements acquired by the EDF (Eléctricité de france) cosmic-ray snow sensors for 4 out of 36 ground stations in the regions of interest. The appropriate optimization method is selected and the corresponding representative optimal coefficients are derived for these four stations. Further, by combining the selected optimization algorithm and the continuous activation function, we propose a new method for deriving the spatially varying coefficients characterizing the entire region, using multi-temporal MODIS snow cover binary maps. When analysed with respect to the mean square error (MSE) criterion, the SWE model, calibrated in this manner, appears to be significantly more accurate than the original version (using a priori estimated, spatially fixed coefficients). Furthermore, the calibration procedure based on MODIS data is comparable and, for some ground stations, exhibits even better performances than the one based on the in situ measurements.

Analysis of supplementary information emerging from the ICA based ICTD

This paper presents an elaboration of the ICA based ICTD, proposed in [1]. The method is applied on three different datasets and three distinctive aspects of its performances are considered. Firstly, we challenge the initial choice of the ICA algorithm, by testing the suitability of two representative tensorial (fourth-order) and one second-order algorithm. Further, we demonstrate the invariance of the proposed decomposition with respect to both the rotation around the line of sight and the change of polarisation basis. Finally, we analyse the potential of supplementary information contained in the second most dominant component.

Sphericity of complex stochastic models in multivariate SAR images

Polarimetry and multi-pass interferometry extend the dimensionality of SAR images, therefore the necessity to have multivariate statistic (and non-Gaussian) distributions as models for these types of data: such are the SIRV (Spherically Invariant Random Vectors). However, as the stochastic model becomes more complex, correctly estimating its parameters gets difficult. More, although they are versatile, the SIRV models are not guaranteed to match the PolSAR / InSAR data. To evaluate the pertinence of those models with respect to the PolSAR and multi-pass InSAR data, through one of their most important statistic properties, namely sphericity, it is the purpose of this paper. The proposed analysis is illustrated with spaceborne multi-pass InSAR TerraSAR-X data.