Communications Internationales

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    Enhancing Echo Processing Through the Integration of Support Vector Machine and Weber’s Law Descriptors
    (SciTePress, 2024) Hedir, Mehdia; Demim, Fethi; Messaoui, Ali Zakaria; Messaoui, Aimen Abdelhak; Belaidi, Hadjira; Rouigueb, Abdenebi; Nemra, Abdelkrim
    Removing ground echoes from weather radar images is a topic of great importance due to their significant impact on the accuracy of processed data. To address this challenge, we aim to develop methods that effectively eliminate ground echoes while preserving the precipitation, which is a crucial meteorological parameter. To accomplish this, we propose to test Local Descriptors based on Weber’s law (WLD), as well as descriptors that combine Weber’s law with Local Binary Pattern (WLBP), using Support Vector Machine (SVM) classifiers to automate the recognition of both types of echoes. The proposed methods are rigorously tested at the sites of Setif and Bordeaux to evaluate their effectiveness in accurately identifying the ground echoes and precipitation. The results of our experiments demonstrate that the proposed techniques are highly effective in eliminating ground echoes while preserving the precipitation, and can be considered satisfactory for practical applications in meteorological data processing.
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    U-Net Based Classification for Urban Areas in Algeria
    (Institute of Electrical and Electronics Engineers, 2020) S.B., Asma; D., Abdelhamid; L., Youyou
    Nowadays, researchers in the field of remote sensing and image classification have to face the challenge of the massive amount of information contained in satellite images, especially in urban areas. These types of areas contain numerous classes, where each class is made of several groups of pixels that are not adjacent, and that are rich in texture. Convolutional Neural Networks possess the ability to handle these problems. However, CNNs require quite a very large number of annotated training samples. U-Net came as a revolutionary solution for this major drawback. This paper aims to study the ability of a pre-trained U-Net to classify a satellite image and is then compared to the performance of a Support Vector Machine classifier
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    Sift and Gabor Features for Very High Resolution Image Classification
    (Institute of Electrical and Electronics Engineers, 2020) Fiala, C.; Daamouche, Abdelhamid
    this 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 alone