Dusan Gleich

Gauss–Markov Model for Wavelet-Based SAR Image Despeckling

This letter presents synthetic aperture radar (SAR) image despeckling using dyadic wavelet transform. Maximum a posteriori (MAP) estimation is used to despeckle a SAR image in the wavelet domain. A wavelet transformed speckle-free image is approximated with a Gauss–Markov random field, and a Gaussian model is chosen to approximate speckle in the wavelet domain. A speckle-free wavelet coefficient is estimated with Bayesian inference using image and noise model parameters, which produce the highest evidence. The experimental results showed that the despeckling algorithm removes speckle noise in the homogeneous areas better than the state-of-the-art methods, which operate in the wavelet and image domain. The proposed method is very simple and computationally not demanding.

Angle of Arrival Estimation Using RSSI and Omnidirectional Rotatable Antennas

This paper proposes a simple novel method for angle of arrival estimation using multiple rotating omnidirectional antennas on a receiver device. The used omnidirectional microstrip antenna has a symmetrical radiation pattern with sharp minimum along the x antenna axis. An algorithm based on the fact that an angle of arrival is obtained along a direction where the measured received strength signal indicator is minimal. Our experimental results for the outdoor measurements reached a mean error of less than 4°, and an indoor of less than 6°.

Despeckling of TerraSAR-X Data Using Second-Generation Wavelets

This letter presents the despeckling of synthetic aperture radar (SAR) images within the bandelet and contourlet domains. A model-based approach is presented for the despeckling of SAR images. The speckle-reduced estimate is found using the first-order Bayesian inference, and the best models parameters are estimated using the second-order Bayesian inference. Synthetic and real images are used for evaluating the qualities of the despeckling methods. The experimental results showed that the combination of Bayesian inference and bandelet transform outperforms the contourlet-based despeckling algorithm using synthetic data and objective measurements.

Wavelet-Based SAR Image Despeckling and Information Extraction, Using Particle Filter

This paper proposes a new-wavelet-based synthetic aperture radar (SAR) image despeckling algorithm using the sequential Monte Carlo method. A model-based Bayesian approach is proposed. This paper presents two methods for SAR image despeckling. The first method, called WGGPF, models a prior with Generalized Gaussian (GG) probability density function (pdf) and the second method, called WGMPF, models prior with a Generalized Gaussian Markov random field (GGMRF). The likelihood pdf is modeled using a Gaussian pdf. The GGMRF model is used because it enables texture parameter estimation. The prior is modeled using GG pdf, when texture parameters are not needed. A particle filter is used for drawing particles from the prior for different shape parameters of GG pdf. When the GGMRF prior is used, the particles are drawn from prior in order to estimate noise-free wavelet coefficients and for those coefficients the texture parameter is changed in order to obtain the best textural parameters. The texture parameters are changed for a predefined set of shape parameters of GGMRF. The particles with the highest weights represents the final noise-free estimate with corresponding textural parameters. The despeckling algorithms are compared with the state-of-the-art methods using synthetic and real SAR data. The experimental results show that the proposed despeckling algorithms efficiently remove noise and proposed methods are comparable with the state-of-the-art methods regarding objective measurements. The proposed WGMPF preserves textures of the real, high-resolution SAR images well.

Evaluation of Bayesian Despeckling and Texture Extraction Methods Based on Gauss–Markov and Auto-Binomial Gibbs Random Fields: Application to TerraSAR-X Data

Speckle hinders information in synthetic aperture radar (SAR) images and makes automatic 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. The first 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 using both supervised and unsupervised classifications, and confusion matrices. 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.

Markov Random Field Models for Non-Quadratic Regularization of Complex SAR Images

This paper presents a comparison between Markovian models for Synthetic Aperture Radar (SAR) image despeckling within the complex domain. The novelty of this paper is enhancement of single look complex SAR images and information extraction. The Gauss-Markov Random Field model, Auto-binomial and Huber-Markov Models are used with the non-quadratic regularization. The experimental results using synthetic generated images and real SAR images showed that the best results were obtained with the Auto-binomial model followed by the Gauss-Markov Random field, and finally the Huber-Markov model, for synthetic generated data and real single look complex SAR images.

Huber–Markov Model for Complex SAR Image Restoration

This letter presents the despeckling of single-look complex (SLC) synthetic aperture radar (SAR) images using nonquadratic regularization. The objective function consists of an image model, a gradient, and a prior model. The Huber-Markov random field (HMRF) models the prior. A numerical solution is achieved through extensions of half-quadratic regularization methods using complex-valued SAR data. The proposed method using the HMRF prior together with nonquadratic regularization shows the superior results on SLC synthetic and actual SAR images.

Soil Moisture Estimation Using High-Resolution Spotlight TerraSAR-X Data

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.

SAR Image Categorization Using Parametric and Nonparametric Approaches Within a Dual Tree CWT

This letter presents synthetic aperture radar (SAR) image classification based on feature descriptors within the discrete wavelet transform (DWT) domain using parametric and nonparametric features. The DWT enables an efficient multiresolution description of SAR images due to its geometric and stochastic features. A 2-D DWT, a real 2-D oriented dual tree wavelet transform (2-D RODTWT) and an oriented dual tree complex wavelet transform (2-D ODTCWT) were used for the estimation of subband features. First and second moments, entropy, coding gain, and fractal dimension were used for the nonparametric approach. A parametric approach considers a Gauss Markov Random Field model for feature extraction. A database with 2000 images representing 20 different classes with 100 images per class was used for classification efficiency assessment. Several SAR scenes were divided into small patches with dimension of 200 × 200 pixels. 10% and 20% of the test images per class were used during the learning stage. Supervised learning using a support vector machine was used for all experiments. The experimental results showed that the proposed methods had superior performances compared with (GLCM) and log comulants of Fourier transform. Amongst the proposed methods, the nonparametric features within oriented dual tree complex wavelet transform gave the best results for classes when categorizing SAR images.

Progressive space frequency quantization for SAR data compression

The authors propose a new wavelet image coding technique for synthetic aperture radar (SAR) data compression called a progressive space-frequency quantization (PSFQ). PSFQ performs spatial quantization via rate distortion-optimized zerotree pruning of wavelet coefficients that are coded using a progressive subband coding technique. They compared the performances of zerotree-based methods: EZW, SPIHT, SFQ, and PSFQ with the classical wavelet-based method (CWM), which uses uniform scalar quantization of subbands followed by recency rank coding. The performances of the methods based on zerotree quantization were better than the CWM in the rate distortion sense. The embedded coding techniques perform better SNR results than the methods using scalar quantization. However, the probability density function (PDF) of the reconstructed amplitude SAR data compressed using CWM, better corresponded to the PDF of the original data than the PDF of the reconstructed data compressed using the zerotree based methods. The amplitude PDF of the reconstructed data obtained using PSFQ compression algorithm better corresponded to the original PDF than the amplitude PDF of the data obtained using the multilook method.