Publications Scientifiques
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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 Optimization of matched filters for VHR image segmentation(Taylor and Francis, 2022) Semcheddine, Belkis Asma; Daamouche, AbdelhamidThe availability of very high resolution remotely sensed images has made the use of spectral information alone insufficient for class recognition, so the integration of spatial information into the segmentation task becomes necessary. Spatial information takes into account the surrounding of a pixel, rather than dealing with a pixel as an isolated item. In this paper, inspired by matched filtering theory, we propose a new technique for spatial feature extraction. The technique consists of using 2D kernels convolved with the spectral bands. The convolution operation bears valuable spatial information about the pixel under consideration. The concatenation of the extracted spatial features and spectral features of pixels feeds a support vector machine (SVM) to segment the image of interest. To cope with the complexity of images having objects of varying sizes (i.e. urban areas), we adopted a hierarchical strategy. That is, kernels with increasing size are applied to extract different spatial features corresponding to different objects. Thus, we have extended the use of matched filters from single object enhancement to multi-object enhancement. A challenging step in designing the matched filters is the two-dimensional kernels coefficients selection, which we formulated as an optimization problem within a particle swarm optimization framework. The optimization process was driven by two different fitness functions, the cross-validation SVM accuracy and the Bhattacharyya distance, which were both evaluated on training samples. We assessed the proposed procedure on two very high-resolution images having different spatial resolutions. The obtained results showed significant improvement in terms of the overall classification accuracy (over 10% for both images). Moreover, visual inspection of the segmented images, in addition to pepper and salt elimination, revealed significant improvement in many objects not detected by the spectral method. Our method of extracting spatial features seems to be very efficientItem A deep neural network approach to QRS detection using autoencoders(Elsevier, 2021) Belkadi, Mohamed Amine; Daamouche, Abdelhamid; Melgani, FaridObjective: In this paper, a stacked autoencoder deep neural network is proposed to extract the QRS complex from raw ECG signals without any conventional feature extraction phase. Methods: A simple architecture has been deeply trained on many datasets to ensure the generalization of the network at inference. Results: The proposed method achieved a QRS detection accuracy of 99.6% using more than 1042000 beats which is competitive with all state-of-the-art QRS detectors. Moreover, the proposed method produced only 0.82% of Detection Error Rate using six unseen datasets containing more than 1470000 beats. Thus confirms the high performance of our method to detect QRSs. Conclusion: Stacked autoencoder neural networks are very effective in QRS detection. At inference, our algorithm processes 1042309 beats in less than 25.32 s. Thus, it is favorably comparable with state-of-the-art deep learning methods. Significance: The stacked autoencoder is an efficient tool for QRS detection, which could replace conventional systems to help practitioners make fast and accurate decisions