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This paper presents a new interferometric coherence optimisation approach based on the flower pollination algorithm, called flower pollination interferometric coherence optimisation (FPICO). FPA is a newly developed algorithm based on the pollination process of plants that is used to solve constrained and/or multiobjective optimisation problems. The proposed approach gives us the possibility to control the scattering vectors to be optimised by setting one of its appropriate parameters beforehand. Moreover, the optimised vectors are conforming to the scattering parametrisation, in contrast to previous coherence optimisation methods that are based on eigenvalues problem. Comparisons of FPICO with classical optimisation approaches have shown that the residue rate decreases by more than 70%, which leads to less deformation in the interferometric phase. Furthermore by the enhancement of the phase quality, the results are interesting and provide additional possibilities in contrast to the classical methods such as the optimization of a specific scattering mechanism which may help in scattering decomposition.

Recent interest in dual polarization Synthetic Aperture Radar (SAR) systems, in which a single polarization is transmitted (e.g. linear horizontal or right circular), followed by reception of two orthogonal polarization, has lead to a novel approach to dual-pol SAR, the so-called compact polarimetric imaging (CPSAR) mode. This paper provides techniques that allow construction of pseudo quad-pol information from dual-polarization SAR systems based on a few simple assumptions. Compact polarimetry showed promise of being able to reduce the complexity, cost, mass, and data rate of a SAR system while attempting to maintain many capabilities of a fully polarimetric system.

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.

Polarization orientation angle (POA) shifts induced by the variations of range and azimuth slopes cause the polarization to rotate about the radar line of sight. Their existence reduces the accuracy measurement of geophysical parameters from polarimetric synthetic aperture radar (PolSAR) images and may generate erroneous scattering characteristics that could be misinterpreted. In real situations, terrain slopes rotate the polarization basis of the polarimetric scattering matrices by an orientation angle shift, and induce significant cross-polarization power. Consequently, it is desirable to compensate the data for the orientation effect before applying scattering model-based decompositions. In this paper, we investigate the compensation effect on the elements of the coherency matrix using the orientation angle extracted from circular polarization technique and from the copolarization signature applied to building areas. The effect of this compensation is that the volume scattering power is consistently decreased, while the double-bounce power is increased. The surface and helix scattering powers are roll invariant. Comparing both methods, we find that the circular polarization algorithm gives mostly the best results except for some targets. In this way, a combined use of both algorithms has been proposed to choose an optimum orientation angle, which can be used directly to compensate POLSAR data to ensure improvement in the overall polarimetric decomposition and classification. We illustrate our results using the polarimetric SAR images acquired on the Algiers city by the RadarSAT2 (FQ19) in C-band.

We present in this paper a Compressed Sensing (CS) proposal technique tested and evaluated on a Synthetic Aperture Radar (SAR) image. The image signals have to present on a few non-zero values. Many papers were proposed in this field and most of them were tested on sparse simulated images. This paper purpose is to make CS applicable on any kind of data especially in SAR field. In reality, sparse signals doesn’t exist, and finding a sparse representation may be impossible without an adapted basis or a-priori information. This makes CS unusable on real SAR images. The presented method is an alternative way to find any signal sparsity and avoid a long computing time to generate a dictionary. We combine the FFT transform with a recovery algorithm to compute the sparse representation. This method is tested on a non-sparse real values signal and on Flevoland C-band SAR image. 45% of data are sufficient to recover an exploitable image.

In this paper, we present an approach to retrieve forest height using an adaptive scattering model decomposition technique with PolInSAR data. This method combines the adaptive scattering model decomposition technique and the coherence amplitude method. This procedure enables the retrieval not only of the vegetation parameters but also of the magnitude associated with each mechanism. To test the performance of the proposed method ESAR PolInSAR L band data of Oberpfaffenhofen, in Germany is used to show the accuracy of the forest height estimation.

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 based on the polarimetric interferometric similarity parameter (PISP) . The new model is more adaptive and fits better with both forest and oriented built-up 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.

In this paper, our objective is twofold: first, to assess the potential of the new compact polarimetry imaging radar system called hybrid-polarimetry (CL-pol): circular transmitted polarization and coherent dual linear receive polarizations for full characterization and exploitation of the backscattered field. Useful characteristics that are unique to the hybrid-polarity architecture are invariance to geometrical orientations and minimizing on-board resource requirements. Second, to develop a classification polarimetric method based on the support vector machine (SVM) which uses full- and the compact-pol modes. We present a study of the polarimetric information content derived from the decomposition for the CL-mode using Stokes parameter data products and from Freeman-Durden-decomposition derived from the full-pol imaging mode. We compare SVM classification both among the partial polarimetric datasets and against the full quad-pol dataset. We illustrate our results by using the polarimetric SAR images of Algiers city in Algeria acquired by the RadarSAT2 (FQ19) in C-band.

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 interrferometry 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.

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.

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.

In this paper a practical method is demonstrated for estimating terrain slopes in azimuth and ground range directions for digital elevation model (DEM) generation without any prior knowledge on the terrain by using only one single pass of polarimetric synthetic aperture radar (PolSAR) instead of two-pass or interferometric SAR (INSAR). The basic approach is by combing the orientation angle estimation and a shape-from-shading technique (SFS) which is mostly used by the computer vision community. In particular, when limited PolSAR data are available, this technique provides an alternative way for DEM generation. The polarization orientation angle (POA) is related to both the range and azimulh angles of the tilted surface and radar viewing angle and it can be estimated from the PolSAR data by using the circular polarization method which shows the best performance in computation efficiency and accuracy with respect to the other methods. After terrain slopes in both the range and azimuth directions have been estimated initially by the combination of the POA estimation and the SFS algorithm, a least squares method similar to that used in interferometric phase unwrapping is used to generate the topography. The least squares approach to phase unwrapping obtains an unwrapped solution by minimizing the differences between the discrete partial derivatives of the (wrapped) phase data and the discrete partial derivatives of the unwrapped solution. We illustrate our results by using the polarimetric SAR images acquired in Algeria by the RadarSAT2 (FQ19) in C-band.

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.