Ferdaous Chaabane

A Multitemporal Method for Correction of Tropospheric Effects in Differential SAR Interferometry: Application to the Gulf of Corinth Earthquake

Tropospheric inhomogeneities can form a major error source in differential synthetic aperture radar interferometry measurements, which are used in slow-deformation monitoring. Indeed, variations of atmospheric conditions between two radar acquisitions produce variations in the signal path of two images and, thus, additional fringes on differential interferograms. These effects have a strong influence on interferograms and must be compensated to obtain reliable deformation measurements. This paper presents a methodological approach to reduce at both global and local scales tropospheric contributions directly from differential interferograms. It first requires refined knowledge of the stable scatterers that can only be obtained from the analysis of a large population of multitemporal interferograms. The correction of global-scale atmospheric contribution exploits the correlation between phase and topography. The correction of local artifacts is based on the correlation between interferograms containing one common acquisition. This technique is validated on a database of 81 differential interferograms covering the Gulf of Corinth (Greece) and used to improve the measurements of ground deformation compared to global positioning system measurements

Pixel and region based temporal classification fusion for HR Satellite Image Time Series

Satellite Image Time Series (SITS) are a very useful source of information for geoscientists especially for land cover monitoring. In this paper a new temporal classification approach for High Resolution (HR) SITS is proposed. It suggests a Bayesian combination between a pixel and a region, SVM (Support Vector Machine) based techniques where SVM is considered as a probabilistic classifier. RBF (Radial Basis Function) based SVM kernel is used to classify pixel evolution while a graph based SVM kernel is considered to analyze region temporal behavior.

An empirical model for interferometric coherence

Many are the examples of application of SAR and differential SAR interferometry for topographic mapping and ground deformation monitoring. However, on repeat pass geometry, the performances of these two techniques are limited by the loss of correlation (coherence) between the two radar acquisitions. The lack of coherence causes an additional noise thus a poor estimate of the interferometric phase. The disturbances can be due either to surface changes because of long period cover (temporal decorrelation) or to a too large baseline (spatial decorrelation). In this paper, we propose an empirical model for the estimate of coherence considering separately these two sources of disturbances. Starting from the observations of experimental data, we study the behaviour of coherence according to baseline and period cover in order to express the two terms of correlation. A number of 170 multi temporal and multi baseline differential interferograms covering the same region is used to validate the proposed model.

Correction of local and global tropospheric effects on differential SAR interferograms for the study of earthquake phenomena

The presence of atmospheric contributions in SAR interferograms represents the main limit for the detection of ground deformation movements. This paper presents a methodological approach to reduce at both global and local scales the tropospheric contributions in the interferograms. It first requires the refined knowledge of the permanent scatterers that can only be obtained from the analysis of a large population of interferograms. The correction of global scale atmospheric contribution exploits the correlation between phase and topography and the correction of local artefacts is based on correlation between interferograms containing one common acquisition.

Feature extraction using PCA for VHR satellite image time series spatio-temporal classification

Image feature extraction is a challenging task as it directly affects analysis of Satellite Image Time Series (SITS) which tackles a huge amount of information (spatial and spectral resolution increase). Therefore, in this paper, Principle Component Analysis (PCA) is applied for feature extraction to improve a multitemporal classification approach for Very High Resolution (VHR) SITS. The improved multitemporal classification succeeds to discern between regions behaviors (stable, periodic etc.), which is very useful in land cover monitoring. Experimental tests have been conducted on both synthesized and real SITS. Performance comparison between PCA and Fisher Feature Selection (Fisher-FS) algorithms is established.

SVM spatio-temporal classification of HR satellite image time series using graph based kernel

Satellite Image Time Series (SITS) are a very useful source of information for geoscientists especially for land cover monitoring. In this paper a new multi-temporal classification approach for High Resolution (HR) SITS is proposed. It is mainly two stages original approach using two different kernels based SVM algorithms. The first step of this approach consists in applying multiband RBF kernel based SVM classification on individual images. Then, for each cartographic region of the first classified image, a graph characterizing its temporal evolution is built using texture features and radiometry for graph labeling. In the second stage, a graph kernel based SVM algorithm is used to analyze and classify the temporal behaviors of these regions that are modeled by different graphs aspects. The resulted temporal map discern between cartographic regions behaviors (stable, periodic, growing, etc.), which is very beneficial in many applications fields. The experimental results have been conducted on synthesized and real data proving the accuracy of the proposed approach.

Application of DSM theory for SAR image change detection

Synthetic Aperture Radar (SAR) data enables direct observation of land surface at repetitive intervals and therefore allows temporal detection and monitoring of land changes. However, the problem of radar automatic change detection is made more difficult, mainly with the presence of speckle noise. This paper presents a new method for SAR image change detection using the Dezert-Smarandache Theory (DSmT). First, a Gamma distribution function is used to characterize globally the radar texture data and allows mass assignment throw Kullback-Leibler distance. Then, local pixel measurements are introduced to refine the mass attribution and take into account the context information. Finally, DSmT is carried out by comparing the modelling results between temporal images. The originality of the proposed method is on the one hand, the use of DSmT which achieve a plausible and paradoxical reasoning comparing to classical Dempster-Shafer Theory (DST). On the other hand, the given approach characterizes the radar texture data with a Gamma distribution which allows a better representation of the speckle. The radar texture is being usually modeled by a Gaussian model in previous DST and DSmT fusion works.

Combination of multiple interferograms for monitoring temporal evolution of ground deformation

SAR and differential SAR interferometry are operational tools for monitoring surface deformation and topographic profile reconstruction. However, they still have limitations due to temporal and geometric decorrelation. These disturbances strongly compromise the accuracy of the results, but reliable measurements can be obtained over a large multitemporal population of interferograms. In this paper we propose a new algorithm for monitoring temporal evolution of ground surface using several interferograms covering ground movements over a long period of time. It is based on a statistical approach with hypothesis test. The objective of multiple interferograms elevation retrieval is to deal with noisy data. Another important advantage of the multiple image processing is that all baselines (small or large) are considered. The method described in this paper can he applied in both SAR and differential SAR interferometry context. We chose to study the SAR interferometry case

Knowledge-based approach for VHR satellite image time series analysis

As satellite data volumes are growing thanks to technological evolution, there are more needs to automatic approaches for Satellite Image Time Series (SITS) analysis. In this article, we propose a new approach based on experts knowledge for land cover monitoring. This approach helps geoscientists to overcome direct interpretation difficulties by modeling expert knowledge. As a first step, using predefined nomenclature and a prior information, concepts are set. Then, each SITS region temporal evolution, represented by a graph, is assigned to the most similar reference one in the knowledge database using the marginalized graph kernel similarity measure.

Morphological filtering of SAR interferometric images

This paper proposes a new morphological filter for SAR interferograms. It is based on a modified version of alternate sequential filters with reconstruction (MASF), in which the structuring elements are adaptively defined according to the fringe directions. This provides a good fidelity to the fringe information while efficiently removing noise. Another feature of the proposed approach is to apply the filter on the original interferogram and on shifted version, to overcome the wrapping of the phase, and to combine the two results. The proposed filtering technique is then tested on both simulated and real data with different levels of noise. It is also compared to previous techniques according to simplicity and noise reduction.