Browsing by Author "S.B., Asma"

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    An Object-Based Approach to VHR Image Classification
    (Institute of Electrical and Electronics Engineers, 2020) S.B., Asma; D., Abdelhamid
    This paper introduces a novel method for the classification of very high resolution, multispectral, remote sensing images. We combine the advantages of both pixel-based and object-based classification techniques. First, the pixels contained in the image are grouped into different batches, called segments, using the algorithm of superpixels. Then the superpixels are merged into more significant objects using one distance metrics among a variety. Finally, the resulting image is classified by the Support Vector Machines classifier. The performance of the proposed approach is compared to the classical spectral-based classification. Using overall accuracy and average accuracy, the results obtained on a high resolution multispectral Boumerdes image reveal the efficiency of the proposed method
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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