Dominant Scattering Mechanism Identification from Quad-Pol-SAR Data Analysis

Polarimetric decompositions are used to separate scatterers and identify their physical parameters by analyzing backscattering, coherence, or covariance matrices. Each cell within polarimetric SAR data is seen as a coherent or incoherent combination of different scattering mechanisms. However, targets are not perfectly characterized by these matrices due to the presence of noise components. The main objective of this study is to remedy the latest issue through proper noise effect elimination. Hence, we propose the re-estimation of the coherence matrix, by incorporating a processing phase that searches for the number of elementary scattering mechanisms in each cell. This first step is based on the eigenvalues, which exploit the advantage of polarization basis independent of the eigenvectors. In the second step, a reduced space is defined by the eigenvectors selected, according to the cases of the first step, as those contributing to the construction of the target, excluding those judged to contribute to noise. The characteristic vector and/or the coherence matrix of the average target is then reconstructed in this new space in three different ways: summation of the elementary coherence matrices, applying Bernoulli's probability law, and orthogonal projection on the reduced space. Finally, the Freeman Durden polarimetric decomposition and the H-alpha Wishart classification are used to show the effectiveness of the process in terms of dominant scattering mechanism identification. Their application on simulated data and on fully-polarized RadarSat-2 images of the city of Algiers attests to the performance of the proposed methodology to improve the identification of dominant scattering mechanisms

Amelioration de la classification supervisee des donnees satellitaires par un choix optimal de caracteristiques

La mise au point de méthodes susceptibles de fournir des caractéristiques spectrales précises est primordiale pour la classification supervisée des données satellitaires. Lorsque l'on n'a que la forme analytique des fonctions de densités de probabilités des classes connues à priori, le problème de la classification se pose en termes d'estimation exacte des paramètres des densités et de discrimination spectrale des classes. Pour une meilleure classification, on doit établir un compromis entre la réduction des données et la sélection des caractéristiques discriminantes

Belhadj-Aissa: Contextual classification of remotely sensed data using MAP approach and

Classification of land cover is one of the most important tasks and one of the primary objectives in the analysis of remotely sensed data. Recall that the aim of the classification process is to assign each pixel from the analysed scene to a particular class of interest, such as urban area, forest, water, roads, etc. The image resulting from the labelling of all pixels is henceforth referred to as “a thematic map”. Such maps are very useful in many remote sensing applications especially those concerned with agricultural production monitoring, land change cover and environmental protection. Conventional classification methods commonly named “punctual methods”, classify each pixel independently by considering only its observed intensity vector. The result of such methods has often “a salt and pepper appearance ” which is a main characteristic of misclassification. In particular of remotely sensed satellite imagery, adjacent pixels are related or correlated, both because imaging sensors acquire significant portions of energy from adjacent pixels and because ground cover types generally occur over a region that is large compared with the size of a pixel. It seems clear that information from neighbouring pixels should increase the discrimination capabilities of the pixel-based measured data, and thus, improve the classification accuracy and the interpretation efficiency. This information is referred to as the spatial contextual information. In recent years, many researchers have proven that the best methodological framework which allows integrating spatial contextual information in images classification is Markov Random Fields (MRF). In this paper, we shall present a contextual classification method based on a maximum a posterior (MAP) approach and MRF. An optimisation problem arises and it will solved by using an optimisation algorithm such as Iterated Conditional Modes (ICM) which occurs the definition and the control of some critical parameters: neighbouring size, regularisation parameter valu

Investigation of the capability of the Compact Polarimetry mode to Reconstruct Full Polarimetry mode using RADARSAT2 data

Recently, there has been growing interest in dual-pol systems that transmit one polarization and receive two polarizations. Souyris et al. proposed a DP mode called compact polarimetry (CP) which is 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. This paper provides a comparison of the information content of full quad-pol data and the pseudo quad-pol data derived from compact polarimetric SAR modes. A pseudo-covariance matrix can be reconstructed following Souyris’s approach and is shown to be similar to the full polarimetric (FP) covariance matrix. Both the polarimetric signatures based on the kennaugh matrix and the Freeman and Durden decomposition in the context of this compact polarimetry mode are explored. The Freeman and Durden decomposition is used in our study because of its direct relationship to the reflection symmetry. We illustrate our results by using the polarimetric SAR images of Algiers city in Algeria acquired by the RadarSAT2 in C-band

Surface Deformation Monitoring from SAR Interferometry for Seismic Hazard Assessment Around Major Active Faults: Case of Zemmouri Earthquake (Algeria)

In seismogenic zones, geodetic campaigns are usually used to monitor the ground deformation. Nowadays, satellite imaging became a powerful tool to monitor surface deformation on very large areas using InSAR (Interferometry Synthetic Aperture Radar). To get reliable results, reducing errors became a real challenge. We recently proposed a new phase unwrapping procedure and a new technique to reduce atmospheric errors. These techniques were assembled to develop a new InSAR time series methods named ISBAS (Improved Small BAseline Subsets). We tested our procedure on Zemmouri (Algeria) seismogenic zone struck by an Mw 6.8 earthquake in 2003. Several SAR images from ENVISAT satellite were used in combination with data from MERIS tool embed- ded on the same satellite to monitor post-seismic deformation. Our analysis highlights the zones of signif- icant deformation due to not only post-seismic seismic movement, but also to other anthropogenic origin. This kind of studies can really help to better assess the seismic hazard around major active fault such as that of Zemmouri and on other active sources that could generates strong seismic events

Non-Parametric Tomographic SAR Reconstruction via Improved Regularized MUSIC

Height estimation of scatterers in complex environments via the Tomographic Synthetic Aperture Radar (TomoSAR) technique is still a valuable research field. The parametric spectral estimation approach constitutes a powerful tool to identify the superimposed scatterers with different complex reflectivities, located at different heights in the same range–azimuth resolution cell. Unfortunately, this approach requires prior knowledge about the number of scatterers for each pixel, which is not possible in practical situations. In this paper, we propose a method that analyzes the scree plot, generated from the spectral decomposition of the multidimensional covariance matrix, in order to estimate automatically the number of scatterers for each resolution cell. In this context, a properly improved regularization step is included during the reconstruction process, transforming the parametric MUSIC estimator into a non-parametric method. The experimental results on two data sets covering high elevation towers, with different facade coating characteristics, acquired by the TerraSAR-X satellite highlighted the effectiveness of the proposed regularized MUSIC for the reconstruction of high man-made structures compared with classical approaches

The Mediterranean region under climate change

This book has been published by Allenvi (French National Alliance for Environmental Research) to coincide with the 22nd Conference of Parties to the United Nations Framework Convention on Climate Change (COP22) in Marrakesh. It is the outcome of work by academic researchers on both sides of the Mediterranean and provides a remarkable scientific review of the mechanisms of climate change and its impacts on the environment, the economy, health and Mediterranean societies. It will also be valuable in developing responses that draw on “scientific evidence” to address the issues of adaptation, resource conservation, solutions and risk prevention. Reflecting the full complexity of the Mediterranean environment, the book is a major scientific contribution to the climate issue, where various scientific considerations converge to break down the boundaries between disciplines