Browsing by Author "Mounir, Zakaria"

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    Spectral-spatial features for hyperspectral image classification
    (2018) Mounir, Zakaria; Merouani, Mawloud; Daamouche, A. (Supervisor)
    Image classification is one among important branches of artificial intelligence field. Generally, it translates the information contained in images into thematic categories which are suitable for use in many applications using low-level visual features. Nowadays, there exists a large number of machine learning algorithms used for image classification. The main objective of this work is to perform a classification of hyperspectral data by means of spectral-spatial features. The principle component analysis was exploited as a tool to decorrelate and reduce the dimension of the original hyperspectral data. The mathematical morphology is used to extract the spatial features; its parameters were generated empirically. The combination of the morphological features and the spectral features were fed to the state-of-the-art classifier which is the Support Vector Machines (SVM). The obtained results over two benchmark datasets show that the achieved performance using the developed method is promising.
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    Spectral-spatial features for hyperspectral image classification
    (2018) Mounir, Zakaria; Merouani, Mawloud; Daamouche, A.(Supervisor)
    Image classification is one among important branches of artificial intelligence field. Generally, it translates the information contained in images into thematic categories which are suitable for use in many applications using low-level visual features. Nowadays, there exists a large number of machine learning algorithms used for image classification. The main objective of this work is to perform a classification of hyperspectral data by means of spectral-spatial features. The principle component analysis was exploited as a tool to decorrelate and reduce the dimension of the original hyperspectral data. The mathematical morphology is used to extract the spatial features; its parameters were generated empirically. The combination of the morphological features and the spectral features were fed to the state-of-the-art classifier which is the Support Vector Machines (SVM). The obtained results over two benchmark datasets show that the achieved performance using the developed method is promising.