Mounira Ouarzeddine

Extraction of Urban Areas From Polarimetric SAR Imagery

Polarimetric synthetic aperture radar (PolSAR) images are extensively used for land-use/land-cover (LULC) classification. One of the important issues in radar remote sensing is urban area detection, where difficulties are found because of its heterogeneity. In this paper, we are interested in urban area detection using PolSAR images which allow us detecting the scattering mechanisms by the use of polarimetric target decompositions methods. We propose in this paper two methods: in the first one, we use the powers of Yamaguchi four-component decomposition and in the second method, we use the coefficients of PolSAR covariance matrix calculated in the circular polarization basis. We added in each method the complex Wishart maximum likelihood (ML) classifier to refine the classification results. To validate both methods, we used two PolSAR images acquired in C-band by RADARSAT-2 satellite over the El Hamiz city in Algeria and San Francisco Bay. The two proposed algorithms give accurate results in both test sites, with superiority of the circular condition method.

Urban areas detection using polarimetric SAR images

In this paper we analyze the urban areas detection by using the polarimetric Synthetic Aperture Radar (SAR) data, thus we propose an algorithm for urban areas detection. The proposed algorithm for urban areas detection uses a comparison between the scattering powers computed from the Yamaguchi four components decomposition as a condition to discriminate data in urban and non-urban. Then we use the maximum likelihood complex Wishart classifier to improve the result. For the validation of our method we have used polarimetric SAR data acquired in C band by RadarSAT-2 over the city of Algiers in Algeria.

Interferometric Coherence Optimization: A Comparative Study

The Interferometric coherence measures the degree of similarity or the correlation between the radar signals corresponding to complex SAR images viewed from close angles. The strong dependence of the interferometric coherence to the polarization state can consider that there is a combination of polarization to achieve maximum interferometric coherence. The methods developed in this field are based on the combination of information derived from polarimetric channels of the interferometric couple and define new projection vectors that maximize the highest possible value of coherence. In this paper we investigated two interferometric coherence optimization methods. The first one is based on the selection of the best pair of projection vectors that maximizes the coherence. The corresponding algorithm leads to an eigenvectors / eigenvalues problem, where each vector represents a scattering mechanism used for view. The second method used an identical projection vector for the two images representing a unique scattering mechanism. To evaluate this work, we used a couple of interferometric airborne polarimetric data acquired on the Tapajos in Brazil, in the P band. Results compared to classical coherence reveals that both methods give better results than single acquisition, however, the first one is the best.

Assess the Effects of Wind on Forest Parameters Inversion by Using Pol-InSAR Applications

A most critical factor that should be taken into consideration for a successful implementation of Pol-InSAR parameter inversion is the temporal baseline decorrelation, which are caused by changes within the scene occurring in the time between acquisitions, especially in the case of repeat-pass space-borne measurements.

Covariance symmetries detection in PolInSAR data

In the last two decades, the use of synthetic aperture radar (SAR) for remote sensing purposes has significantly developed due to improvements in the quality and the availability of the images. Two powerful SAR techniques, namely, polarimetry and interferometry, have further increased the range of applications of the sensed data. Using polarimetry, geometrical properties and geophysical parameters, such as shape, roughness, texture, and moisture content, can be retrieved with considerable accuracy, while interferometric information may be used to extract vertical information with accuracy less than 1 cm. In this paper, the potential of using joint polarimetry and interferometry techniques in SAR data (PolInSAR) for the purpose of SAR image classification is investigated. To achieve this goal, we extend a covariance symmetry detection framework to the PolInSAR scenario. The proposed approach will be shown to be able to exploit the peculiar structures of the covariance matrices of PolInSAR images to discriminate structures within the image. Results using real-SAR data are presented to validate the effectiveness of the proposed approach.

Volume Height Estimation based on Fusion of Discrete Fourier Transform (DFT) and Least Square (LS) in a Tomographic SAR Application

Tomographic-SAR (Synthetic Aperture Radar) is a 3D Radar imaging technique, based on spectral estimation tools. This technique is used to estimate the distribution of the backscattering signal in the elevation axis, for each azimuth-range resolution cell of the SAR image. Spectral estimation algorithms belong to two families, non parametric estimation algorithms which include DFT (Discrete Fourier Transform), SVD (Single Value Decomposition), MUSIC (Multiple Signal Classification), CAPON and parametric estimation algorithms such as LS (Least Square) and ESPRIT (Estimation of signal parameters via rotation invariance techniques). In this paper we present an inversion algorithm based on the fusion of DFT and LS for the estimation of the reflectivity signal along the elevation axis. With an appropriate combination of these two algorithms and a realistic modeling of the signal distribution, we obtain a high resolution estimate of the reflectivity signal with medium computational effort. The inversion algorithm is tested on a forested area (Västerbotten in northern Sweden), with multibaseline data set acquired in L-band (BioSAR-2008 project). Results are promising with the proposed algorithm. We used MUSIC and RVoG (Random Volume over Ground) inversions for comparison and LIDAR (Laser Imaging Detection And Ranging) image as datasets for validation of the results.

Adaptive model based on polarimetric decomposition using correlation coefficient in horizontal–vertical and circular basis

Abstract. This paper presents two decomposition schemes for polarimetric synthetic aperture radar data. The proposed schemes intend to overcome the problem of scattering ambiguity and reduce the volume scattering power in oriented urban areas. The first proposed scheme uses an empirical volume model based on the correlation coefficients of the Pauli component in the horizontal–vertical basis, whereas the second one employs a volume model defined on correlation coefficients of the Pauli components expressed in the circular basis. The correlation coefficients are calculated from polarimetric interferometric synthetic aperture radar (PolInSAR) data. The characteristics adopted from these volume models are used to enhance the results of the decomposition schemes. The scattering powers estimated from the proposed methods give promising results compared to existing methods in the literature, particularly in urban areas since all the oriented built-up areas are well discriminated as double or odd bounce scattering. The methods are evaluated using the experimental airborne SAR sensor (E-SAR) PolInSAR L band data acquired on the Oberpfaffenhofen test site in Germany.

Adaptive model-based polarimetric decomposition using correlation coefficient

In this paper, we present a new approach to solve the problem of volume scattering ambiguity in urban area, for that we propose a volume model on the correlation coefficient of pauli component (HH-VV) using polarimetric sar interferometry PolInSAR data. The new model is more adaptive and fits better with both forest and oriented builtup areas. Thereby, a new model-based polarimetric decomposition scheme is developed. To test the performance of the proposed method ESAR PolInSAR L bande data of Oberpfaffenhofen, Germany is used. Comparison experiments show that the proposed method gives good results, since all the oriented built-up areas are well discriminated as double or odd bounce structures.

Classification of multi-look polarimetric SAR imagery based on the automated EM Gaussian clustering algorithm using the complex Wishart distribution

In statistical classification, such mixture models allow a formal approach to unsupervised clustering. Fitting mixture distributions can be handled by a wide variety of techniques. A standard method to fit finite mixture models to observed data is the Expectation-Maximization (EM) algorithm which is an iterative procedure which converges to a (local) maximum of the marginal a posteriori probability function. In this paper we provide a review on the Gaussian classification for polarimetric images. In this review work, the analysis of multi-look polarimetric covariance matrix data uses an automated statistical clustering method based upon the expectation maximization (EM) algorithm for finite mixture modeling, using the complex Wishart probability density function. This classification technique is compared to that obtained with the most known standard H/ decomposition combined to the Wishart distribution which gives 8 fixed classes identified from the H/ space, in opposite to the EM-Wishart classifiaction which automatically determines the number of statistically distinct clusters in finite mixture modeling in an image using the Goodness-of-fit (GoF) and more than eight classes can be identified. Both approaches conducted on the polarimetric images show very convincing clustering results.

The effect of desying angle on polarimetric SAR image decomposition

Polarimetric image decomposition is nowadays among the most important applications of multi-polarization, multifrequency SAR radar images. With the growth of new satellite missions equipped with fully polarimetric modes there is a strong need for accurate methods and for new approaches to handle the huge data coming from different airborne and space borne missions and to understand better the several and different mechanisms that occur in a resolution cell. We are interested in this paper in polarimetric SAR image decomposition that makes a comparison between Yamaguchi decomposition called also the four component decomposition before and after image compensation from the orientation angle. This latter affects directly the scattering mechanisms and induces errors in the decomposition results especially in urban area where there are complex structures. We demonstrate with power profiles and with RGB color composite images that the volume scattering type decreases drastically after deorientation, whereas the helix scattering type is not sensitive to orientation. The test site is situated in the north of Algiers city and the satellite data is a fully polarimetric acquisition in C band. Results are in a high agreement with Google earth optical image.