Communications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/11

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Author: search.filters.author.Daamouche, AbdelhamidHas files: No

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    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, Abdelhamid
    This 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.
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    CNN and M-SLIC superpixels feature fusion for VHR image classification
    (IEEE, 2022) Semcheddine, Belkis Asma; Daamouche, Abdelhamid
    In 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)