Semcheddine, Belkis AsmaDaamouche, Abdelhamid2023-05-152023-05-152022DOI: 10.1109/ICATEEE57445.2022.10093756https://ieeexplore.ieee.org/document/10093756/authors#authorshttps://dspace.univ-boumerdes.dz/handle/123456789/11512In 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)enCNNFeature fusionM-SLIC superpixels segmentationHaralick featuresMultispectral image classificationRemote sensingOther