10,828 research outputs found

    Terrain analysis using radar shape-from-shading

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    This paper develops a maximum a posteriori (MAP) probability estimation framework for shape-from-shading (SFS) from synthetic aperture radar (SAR) images. The aim is to use this method to reconstruct surface topography from a single radar image of relatively complex terrain. Our MAP framework makes explicit how the recovery of local surface orientation depends on the whereabouts of terrain edge features and the available radar reflectance information. To apply the resulting process to real world radar data, we require probabilistic models for the appearance of terrain features and the relationship between the orientation of surface normals and the radar reflectance. We show that the SAR data can be modeled using a Rayleigh-Bessel distribution and use this distribution to develop a maximum likelihood algorithm for detecting and labeling terrain edge features. Moreover, we show how robust statistics can be used to estimate the characteristic parameters of this distribution. We also develop an empirical model for the SAR reflectance function. Using the reflectance model, we perform Lambertian correction so that a conventional SFS algorithm can be applied to the radar data. The initial surface normal direction is constrained to point in the direction of the nearest ridge or ravine feature. Each surface normal must fall within a conical envelope whose axis is in the direction of the radar illuminant. The extent of the envelope depends on the corrected radar reflectance and the variance of the radar signal statistics. We explore various ways of smoothing the field of surface normals using robust statistics. Finally, we show how to reconstruct the terrain surface from the smoothed field of surface normal vectors. The proposed algorithm is applied to various SAR data sets containing relatively complex terrain structure

    Efficient Approach for OS-CFAR 2D Technique Using Distributive Histograms and Breakdown Point Optimal Concept applied to Acoustic Images

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    In this work, a new approach to improve the algorithmic efficiency of the Order Statistic-Constant False Alarm Rate (OS-CFAR) applied in two dimensions (2D) is presented. OS-CFAR is widely used in radar technology for detecting moving objects as well as in sonar technology for the relevant areas of segmentation and multi-target detection on the seafloor. OS-CFAR rank orders the samples obtained from a sliding window around a test cell to select a representative sample that is used to calculate an adaptive detection threshold maintaining a false alarm probability. Then, the test cell is evaluated to determine the presence or absence of a target based on the calculated threshold. The rank orders allows that OS-CFAR technique to be more robust in multi-target situations and less sensitive than other methods to the presence of the speckle noise, but requires higher computational effort. This is the bottleneck of the technique. Consequently, the contribution of this work is to improve the OS-CFAR 2D with the distributive histograms and the optimal breakdown point optimal concept, mainly from the standpoint of efficient computation. In this way, the OS-CFAR 2D on-line computation was improved, by means of speeding up the samples sorting problem through the improvement in the calculus of the statistics order. The theoretical algorithm analysis is presented to demonstrate the improvement of this approach. Also, this novel efficient OS-CFAR 2D was contrasted experimentally on acoustic images.Fil: Villar, Sebastian Aldo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarría. Departamento de Electromecánica. Grupo INTELYMEC; ArgentinaFil: Menna, Bruno Victorio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarría. Departamento de Electromecánica. Grupo INTELYMEC; ArgentinaFil: Torcida, Sebastián. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Departamento de Matemática; ArgentinaFil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarría. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentin

    Comparative Study of the Descriptive Experiment Design and Robust Fused Bayesian Regularization Techniques for High-Resolution Radar Imaging

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    In this paper, we perform a comparative study of two recently proposed high-resolution radar imaging paradigms: the descriptive experiment design regularization (DEDR) and the fused Bayesian regularization (FBR) methods. The first one, the DEDR, employs aggregation of the descriptive regularization and worst-case statistical performance (WCSP) optimization approaches to enhanced radar/SAR imaging. The second one, the FBR, performs image reconstruction as a solution of the ill- conditioned inverse spatial spectrum pattern (SSP) estimation problem with model uncertainties via unifying the Bayesian minimum risk (MR) estimation strategy with the maximum entropy (ME) randomized a priori image model and other projection-type regularization constraints imposed on the solution. Although the DEDR and the FBR are inferred from different descriptive and statistical constrained optimization paradigms, we examine how these two methods lead to structurally similar techniques that may be further transformed into new computationally more efficient robust adaptive imaging methods that enable one to derive efficient and consistent estimates of the SSP via unifying both the robust DEDR and FBR considerations. We present the results of extended comparative simulation study of the family of the image formation/ enhancement algorithms that employ the proposed robustified FBR and DEDR methods for high-resolution reconstruction of the SSP in a virtually real time. The computational complexity of different methods are analyzed and reported together with the scene imaging protocols. The advantages of the well designed SAR imaging experiments (that employ the FBR-based and DEDR-related robust estimators) over the cases of poorer designed experiments (that employ the conventional matched spatial filtering as well as the leaCinvestavUniversidad de Guadalajar

    Adaptive Total Variation Regularization Based SAR Image Despeckling and Despeckling Evaluation Index

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