Center des Techniques Spatiales

Oran, Algeria

Center des Techniques Spatiales

Oran, Algeria
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Touati F.,Center des Techniques Spatiales | Benaraba N.,Center des Techniques Spatiales
Inverse Problems in Science and Engineering | Year: 2017

In this paper, a novel inversion method is proposed for jointly robust estimation of parameters and variance components from disjunctive groups of observations affected by outliers. This method, named robust non-negative variance component estimation (RVCE), is a coupling of variance component estimation (VCE) technique with a robust estimation method, developed to cope with outliers and to avoid negative variance, leading thus, to an estimation reliable enough. The principle of RVCE method is based on the refinement of the stochastic model via an equivalent weight matrix established from the original measurement weight matrix and an adapted full weight matrix with hard rejection to outliers. This last one is derived from the robust standardized residuals, using a highly robust estimator, as an initial solution of the inverse problem, and a cut-off value adapted to sort out the good observations from the bad ones. Furthermore, because the original weight matrix is partly known, the integration of the VCE technique plays a key role to reach an optimal solution and to provide valuable information on the precision of the estimates. The performance of the proposed method is demonstrated by considering a rockfill dam as an example, where the material parameters and variance components are jointly estimated from geotechnical and geodetic measurements. The results of comparison study between RVCE method with other methods such as the classical NN-VCE, RIMCO, least squares and the combined Huber’s M-estimator with VCE (HVCE) for various configuration options have highlighted the pertinence of the proposed method. © 2017 Informa UK Limited, trading as Taylor & Francis Group

Djerriri K.,Center des Techniques Spatiales | Karoui M.S.,Center des Techniques Spatiales
2017 Joint Urban Remote Sensing Event, JURSE 2017 | Year: 2017

During the past decades significant efforts have been made in developing various methods for Very high spatial resolution (VHSR) remotely sensed image classification; most of them are based on handcrafted learning-based features. Recently deep learning-based techniques have demonstrated excellent performance in remote sensing applications. In this paper we address the problem of urban imagery classification by developing a convolutional neural network (CNN) approach, which are the most popular deep learning approach for image classification. We design a custom CNN that operates on local patches in order to produce pixel-level classification map. The performance of the proposed model is validated on an exhaustive experimental comparison on a set of 20 QuickBird pansharpened multi-spectral images in urban zones. The obtained results outperform those obtained by different classification approaches on the same dataset. © 2017 IEEE.

Karoui M.S.,Center des Techniques Spatiales | Karoui M.S.,CNRS Institute for research in astrophysics and planetology | Karoui M.S.,University of Science and Technology of Oran | Deville Y.,CNRS Institute for research in astrophysics and planetology | And 2 more authors.
Pattern Recognition | Year: 2012

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. Traditional classification techniques assign only one class (e.g., water, soil, grass) to each pixel of remote sensing images. However, the area covered by one pixel contains more than one surface component and results in the mixture of these surface components. In such situations, classical classification is not acceptable for many major applications, such as environmental monitoring, agriculture, mineral exploration and mining, etc. Most methods proposed for treating this problem have been developed for hyperspectral images. On the contrary, there are very few automatic techniques suited to multispectral images. In this paper, we propose new unsupervised spatial methods (called 2D-Corr-NLS and 2D-Corr-NMF) in order to unmix each pixel of a multispectral image for better recognizing the surface components constituting the observed scene. These methods are related to the blind source separation (BSS) problem, are based on sparse component analysis (SCA), clustering and non-negativity constraints. Our approach consists in first identifying the mixing matrix involved in this BSS problem, by using the first stage of a spatial correlation-based SCA method with very limited source sparsity constraints, combined with clustering. Non-negative least squares (NLS) or non-negative matrix factorization (NMF) methods are then used to extract spatial sources. An important advantage of our proposed methods is their applicability to the possibly globally underdetermined, but locally (over)determined BSS model in multispectral remote sensing images. Experiments based on realistic synthetic mixtures and real multispectral images collected by the Landsat ETM and the Formosat-2 sensors are performed to evaluate the performance of the proposed approach. We also show that our methods significantly outperform the sequential maximum angle convex cone (SMACC) method. © 2012 Elsevier Ltd.

Bentoutou Y.,Center des Techniques Spatiales
IEEE Transactions on Aerospace and Electronic Systems | Year: 2012

Onboard error detection and correction (EDAC) devices aim to secure data transmitted between the central processing unit (CPU) of a satellite onboard computer (OBC) and its local memory. A follow-up is presented here of some low-complexity EDAC techniques for application in random access memories (RAMs) onboard the Algerian microsatellite Alsat-1. The application of a double-bit EDAC method is described and implemented in field programmable gate array (FPGA) technology. The performance of the implemented EDAC method is measured and compared with three different EDAC strategies, using the same FPGA technology. A statistical analysis of single-event upset (SEU) and multiple-bit upset (MBU) activity in commercial memories onboard Alsat-1 is given. A new architecture of an onboard EDAC device for future Earth observation small satellite missions in low Earth orbits (LEO) is described. © 2006 IEEE.

Karoui M.S.,Center des Techniques Spatiales
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2013

This paper presents a new fusion approach for pan-sharpening multispectral remote sensing images. This approach, related to Linear Spectral Unmixing (LSU) techniques, includes Extended Nonnegative Matrix Factorization (ExNMF) for combining low spatial resolution multispectral and high spatial resolution panchromatic data. ExNMF is applied to different real multispectral and panchromatic data sets with different spatial resolutions and different number of spectral bands. The quality of pan-sharpened multispectral images is evaluated by the jointly spectral and spatial Quality with No Reference (QNR) index. Obtained results show that our proposed method outperforms the Principal Component Analysis (PCA) and Gram-Schmidt (GS)-based standard literature methods. © 2013 SPIE.

Gourine B.,Center des techniques spatiales
Comptes Rendus - Geoscience | Year: 2012

The purpose of this article is to study the contribution of a LEO satellite (Starlette) laser measurements in the estimation of the geodetic products, such as station coordinates, Earth Orientation Parameters (EOP), and Geocenter component variations, over a 14 year period (1993-2007). Three data combinations are considered in the processing, namely LAGEOS-1 (LA-1), LAGEOS-1&-2 (LA-1&LA-2) and LAGEOS-1&Starlette (LA-1&STAR). The orbit computation of the different satellites is performed with GINS software and the laser data processing is carried out by MATLO software, with consideration of a recent GRACE gravity model (Eigen_Grace03s) in the Starlette orbit computation. The time series of results are projected according to ITRF2005, by TRANSFOR software, where the Helmert transformation parameters are obtained. A comparison of the different combinations is effectuated in terms of quality, periodic signals and noises of the weekly stations positions, EOP and Geocenter variations. The results revealed a degradation of positioning accuracy of about 3 to 5. mm when using Starlette data according to LA-1&STAR solution, but also a better capability to determine the annual and semi-annual variations of the UP coordinates and Geocenter components. © 2012 Académie des sciences.

Karoui M.S.,Center des Techniques Spatiales | Djerriri K.,Center des Techniques Spatiales | Boukerch I.,Center des Techniques Spatiales
International Journal of Remote Sensing | Year: 2016

Pansharpening aims at combining observable panchromatic and multispectral images to generate an unobservable image with the high spatial resolution of the former and the spectral diversity of the latter. In this paper a new fusion method is proposed. This method, related to linear spectral unmixing (LSU) techniques and based on non-negative matrix factorization (NMF), optimizes, by new iterative–multiplicative update rules, a joint criterion that exploits a spatial degradation model between the two images. The proposed Multiplicative Joint Non-negative Matrix Factorization (MJNMF) approach is applied to synthetic and real data, and its effectiveness in spatial and spectral domains is evaluated with commonly used performance criteria. Experimental results show that the proposed method yields good spectral and spatial fidelities of the pansharpened data. Also, it outperforms those tested from the literature. © 2016 Taylor & Francis.

Benzeniar H.,Center des Techniques Spatiales | Fellah M.K.,University Djilali Liabes
International Review of Aerospace Engineering | Year: 2014

Current interest in small satellites lies in the feasibility of achieving specific but limited objectives, by necessity, small satellites technology requires simple control schemes. In this paper, we present a fuzzy logic controller, as an attempt to replace the classical proportional derivative (PD) controller that is widely used in the spacecraft attitude determination control system (ADCS). The fuzzy logic controller is implemented on the Z axis (yaw) closed loop reaction wheel of a low earth orbit microsatellite with a gravity gradient stabilization. The yaw axis is pointed toward earth. We mention here that, when the spacecraft is not in the imaging mode, it starts spinning along the yaw axis to keep it within thermal limits. So that, in this axis the spacecraft is either in the imaging mode (freeze mode) or barbeque mode (BBQ mode). © 2014 Praise Worthy Prize S.r.l. - All rights reserved.

Djerriri K.,Center des Techniques Spatiales | Mimoun M.,University Djilali Liabes
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2015

This work presents a new approach for automatic discovering of useful spectral transformations in remotely sensed imagery. The method applies an approach based on One-class classification, ISODATA unsupervised classification and Genetic Programming (GP) to combine spectral bands. Experiments on burned areas extraction from Landsat8-Oli images show that the proposed method yields better results than the traditional spectral transformations. © 2015 IEEE.

Bentoutou Y.,Center des Techniques Spatiales
Advances in Space Research | Year: 2011

This paper presents a follow-up of the results of an 8-year study on radiation effects in commercial off the shelf (COTS) memory devices operating within the on-board data handling system of the Algerian micro-satellite Alsat-1 in a Low-Earth Orbit (LEO). A statistical analysis of single-event upset (SEU) and multiple-bit upset (MBU) activity in commercial memories on-board the Alsat-1 primary On-Board Computer (OBC-386) is given. The OBC-386 is an Intel 80C386EX based system that plays a dual role for Alsat-1, acting as the key component of the payload computer as well as the command and control computer for the micro-satellite. The in-orbit observations show that the typical SEU rate at Alsat-1's orbit is 4.04 × 10-7 SEU/bit/day, where 98.6% of these SEUs cause single-bit errors, 1.22% cause double-byte errors, and the remaining SEUs result in multiple-bit and severe errors. © 2011 COSPAR. Published by Elsevier Ltd. All rights reserved.

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