Speckle hinders information in synthetic aperture radar (SAR) images and makesautomatic information extraction very difficult. The Bayesian approach allows us to perform the despeckling of an image while preserving its texture and structures. This model-based approach relies on a prior model of the scene. This paper presents an evaluation of two despeckling and texture extraction model-based methods using the two levels of Bayesian inference. Thefirst method uses a Gauss-Markov random field as prior, and the second is based on an auto-binomial model (ABM). Both methods calculate a maximum a posteriori and determine the best model using an evidence maximization algorithm. Our evaluation approach assesses the quality of the image by means of the despeckling and texture extraction qualities. The proposed objective measures are used to quantify the despeckling performances of these methods. The accuracy of modeling and characterization of texture were determined usingboth supervised and unsupervised classifications, and confusionmatrices. Real and simulated SAR data were used during the validation procedure. The results show that both methods enhance the image during the despeckling process. The ABM is superior regarding texture extraction and despeckling for real SAR images.
COBISS.SI-ID: 15493398
High-resolution and dual polarized Spotlight TerraSAR-X images are assessed for soil moisture parameter retrieval. This letter presents bare soil moisture estimation and estimation of moisture of vegetated areas. The bare soil moisture estimation is based on the Shi model. The Minimum Mean Square Error approach is used to determine the unknown parameters of the Shi model using ground measurements of volumetric moisture and SAR data. The soil moisture of vegetated areas is estimated using the vegetation and soil backscattering coefficients. The unknown parameters of vegetation and soil backscattering models were estimated using Tikhonov optimization. The experimental results showed that the used models provide good results for estimating bare soil moisture and moisture of vegetated areas.
COBISS.SI-ID: 14814230
Synthesis of a simple robust controller with a pole placement technique and a H. metrics is the method used for control of a servo mechanism with BLDC and BDC electric motors. The method includes solving a polynomial equation on the basis of the chosen characteristic polynomial using the Manabe standard polynomial form and parametric solutions. Parametric solutions are introduced directly into the structure of the servo controller. On the basis of the chosen parametric solutions the robustness of a closedloop system is assessed through uncertainty models and assessment of the norm . ... The design procedure and the optimization are performed with a genetic algorithm differential evolution - DE. The DE optimization method determines a suboptimal solution throughout the optimization on the basis of a spectrally square polynomial and Šiljakćs absolute stability test. The stability of the designed controller during the optimization is being checked with Lipatovćs stability condition. Both utilized approaches: Šiljakćs test and Lipatovćs condition, check the robustness and stability characteristics on the basis of the polynomial's coefficients, and are very convenient for automated design of closed-loop control and for application in optimization algorithms such as DE.
COBISS.SI-ID: 14949398