Communications Internationales
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/11
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Item Convolutional Encoder-Decoder Network for Road Extraction from Remote Sensing Images(Institute of Electrical and Electronics Engineers, 2024) Makhlouf, Yasmine; Daamouche, Abdelhamid; Melgani, FaridIn this paper, we propose a convolutional neural network, which is based on down sampling followed by up sampling architecture for the purpose of road extraction from aerial images. Our model consists of convolutional layers only. The proposed encoder-decoder structure allows our network to retain boundary information, which is a critical feature for road identification. This feature is usually lost when dealing with other CNN models. Our design is also less complex in terms of depth, number of parameters, and memory size. It, therefore, uses fewer computer resources in both training and during execution. Experimental results on Massachusetts roads dataset demonstrate that the proposed architecture, although less complex, competes with the state-of-the-art proposed approaches in terms of precision, recall, and accuracy.Item SIFT and Gabor Based Features Extraction Method Applied to the Infrared VHR Image of Boumerdes(Institute of Electrical and Electronics Engineers, 2024) Fiala, Chahrazed; Daamouche, AbdelhamidThis paper investigates the contribution of SIFT and Gabor features in the classification of a VHR Image, focusing on the Infrared and RGB channels. The assessment of the classification scheme is conducted by the SVM classifier, and the comparison using the average accuracy as a metric reveals that the infrared channel improves the performance of the classification scheme.Item CNN and M-SLIC superpixels feature fusion for VHR image classification(IEEE, 2022) Semcheddine, Belkis Asma; Daamouche, AbdelhamidIn this letter, we present a method for fusing handcrafted features with abstract features for the purpose of VHR remote sensing image classification. The proposed strategy allows for a multi-level feature fusion, which enriches the available spectral data, resulting in a better class separability. In a first step, deep features are extracted using Convolutional Neural Networks. These features are then fused with Haralick features drawn out by means of M-SLIC superpixels segmentation. The combined features are then concatenated with the spectral features of the image and classified using Support Vector Machines. Our experiments were conducted on a VHR satellite image, and the obtained results qualify us to validate the superiority of the suggested scheme (over 16% overall classification accuracy improvement)Item A new method for accurate QRS detection using stationary wavelet transform(Mohamed Amine Belkadi;, 2017) Belkadi, Mohamed Amine; Daamouche, AbdelhamidIt is well-known that the wavelet transform is a very useful mathematical tool for scale analysis, with very accurate frequency components estimation for the input signal. In this paper, we propose a new efficient method for QRS detection by employing the Stationary Wavelet Transform (SWT) also known as short wavelet transform. Our approach has been tested over MIT/BIH benchmark database. The obtained results are in a good agreement with the published works. Globally, we achieved a sensitivity of 99.733%, specificity of 99.922% and an error rate of 0.345% using Lead I ECGItem ECG as a biometric for individual's identification(IEEE, 2017) Sellami, Abdelkader; Zouaghi, Amine; Daamouche, AbdelhamidIn this paper, we investigate a new method to analyze electrocardiogram (ECG) signal, extract the features, for the real time human identification using single lead human electrocardiogram. The proposed system extracts special parts of the ECG signal starting from the P wave, the QRS complex and ending with the T wave for that we used the multiresolution wavelet analysis. Different features are selected and reconstructed from both amplitude and time interval of the ECG signal. The matching decisions are evaluated on the basis of correlation coefficient between the features and the Radial Basis function network classifier is introduced for validation and comparison. The performance evaluation was carried out on four ECG public databases with a total of 149 persons subjected to different physical activities and heart conditions, the preliminary results indicate that the system achieved an accuracy of 90-93%Item Compact and Full Polarimetric SAR Imaging for Target Characterization(Institute of Electrical and Electronics Engineers, 2020) Bouzerar, H.; Mebrek, S.; Souissi, B.; Daamouche, AbdelhamidRecent 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 systemItem Sift and Gabor Features for Very High Resolution Image Classification(Institute of Electrical and Electronics Engineers, 2020) Fiala, C.; Daamouche, Abdelhamidthis paper presents a new approach to extract features from high resolution images inspired by the sift descriptor and gabor features. both of these two methods are powerful when used separately or together in region-based or pixel-based classification, they brought a high accuracy. our approach was applied to classify two very high resolution images of boumerdes (algeria) and djeddah (ksa) using knn and svm. the obtained results achieved promising performance compared to using spectral information aloneItem An improved QRS detection method using Hidden Markov Models(IEEE, 2017) Belkadi, Mohamed Amine; Daamouche, AbdelhamidItem An object detection technique for very high resolution remote sensing images(IEEE, 2013) Moranduzzo, Thomas; Melgani, Farid; Daamouche, Abdelhamid