The motivation of this paper is to address the problem of registering airborne LiDAR data and optical aerial or satellite imagery acquired from different platforms, at different times, with different points of view and levels of detail. In this paper, we present a robust registration method based on building regions, which are extracted from optical images using mean shift segmentation, and from LiDAR data using a 3D point cloud filtering process. The matching of the extracted building segments is then carried out using Graph Transformation Matching (GTM) which allows to determine a common pattern of relative positions of segment centers. Thanks to this registration, the relative shifts between the data sets are significantly reduced, which enables a subsequent fine registration and a resulting high-quality data fusion.

The adequate use of ultrasound permits the measurement of carbon dioxide pressure in a two-phase flow fluid. The proposed method relies on the measurement of radial mixture velocities acquired from ultrasound data by Doppler effect. The gas pressure is calculated knowing the measured radial velocities using Drift flux model and Young-Laplace equations The modeling of system response consists on the determination of elemental acoustic signals through radial mixture velocities and volumetric gas fraction calculated at two positions of the measurement domain. The summation of these signals gives the total responses. The frequency spectra of these signals permits the calculation of the radial mixture velocities and consequently estimate the gas pressure through the mean mixture pressure.

In this paper, we propose a novel model-based approach for 3D underwater scene reconstruction, i.e., bathymetry, for side scan sonar arrays in complex and highly reverberating environments like shallow water areas. The presence of multipath echoes and volume reverberation generates false depth estimates. To improve the resulting bathymetry, this paper proposes and develops an adaptive filter, based on several original geometrical models. This multimodel approach makes it possible to track and separate the direction of arrival trajectories of multiple echoes impinging the array. Echo tracking is perceived as a model-based processing stage, incorporating prior information on the temporal evolution of echoes in order to reject cluttered observations generated by interfering echoes. The results of the proposed filter on simulated and real sonar data showcase the clutter-free and regularized bathymetric reconstruction. Model validation is carried out with goodness of fit tests, and demonstrates the importance of model-based processing for bathymetry reconstruction.

In this paper a 3-D reconstruction method is proposed of sea bottom topography, i.e. bathymetry, for sonar data in highly reverberant and multi-path underwater environments. Recent publications showcase the negative impact of waves involving sea surface reflections on the sea bottom imaging process. The novelty of our proposal is twofold: firstly it relies on the use of Markovian-model based processors for bahymetric reconstruction and involves data association filters. Secondly, we propose a nearest neighbor version of the Integrated Probabilistic Data Association filter, capable of filtering several echo trajectories in the presence of clutter with a relatively reduced complexity compared to other existent methods.

Current sonar and radar applications use interferometry to estimate the arrival angles of backscattered signals. This direction-finding method is based on a phase-difference measurement between two close receiving arrays. To model the associated bathymetric error, it is necessary to know the second-order moment of the interferometric phase estimator. This paper explains the connection between bathymetric variance and interferometric phase variance and the difficulty in the evaluation of the phase second-order estimation. Thus, a brief statistical overview of the interferometric phase estimator for fully developed speckle signals (called here RMPC for random modulus partially correlated signals) is introduced in this paper. The focus of this paper is the derivation of simple empirical variance that matches the exact values in both single-look and multilook cases. For the sake of constituency, the construction of these empirical approximations is based on a modified asymptotic expression of the second order for the phase estimator assuming high signal-to-noise ratio. In order to perform these derivations, it appears necessary to introduce a new kind of signal (namely, CMPC for constant modulus partially correlated signal) whose modulus is assumed constant. This family of signals, whose physical existence is also investigated, appears as an alternative way to derive RMPC empirical formulas. The link between RMPC and CMPC signals is established through the conditional expectation of the signal modulus. Finally, the existence of these two statistical behaviors is tested over real underwater data.

In SAR image processing, taking phase distortion in radar signals into account is equivalent to considering motion as information embedded in it. In the aim of doing this, a model based on Gaussian linear frequency modulation is introduced, adapted to quantification and estimation of phase distortion. This model is based on three parameters, the Doppler frequency modulation rateK, the analysis timeTaand the Doppler frequencyfd, and allows practically a local representation of any quasi-punctal target echo.