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A Deep Convolutional Neural Network based on Local Binary Patterns of Gabor Features for Classification of Hyperspectral Images

To date, various spatial-spectral methods are proposed for accurate classification of hyperspectral images (HSI). Gabor spatial features are the most prominent ones that can extract shallow features such as edges and structures. In recent years, convolutional neural networks (CNN) have been promising in the classification of HSI. Although in literature Gabor features are used as the input of deep models, it seems that the performance of CNN can be improved by two-stage textural features based on local binary patterns of Gabor features. In this paper, input features of CNN are obtained based on local binary patterns of Gabor features which are more discriminative than both Gabor features and local binary patterns features. The experiments performed on the famous Indian Pines HIS, proved the superiority of the proposed method over some other deep learning-based methods.