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Recently, classification and dimensionality reduction (DR) have become important issues of hyperspectral image (HSI) analysis. Especially, HSI classification is a challenging task due to the high-dimensional feature space, with a large number of spectral bands, and a low number of labeled samples. In this paper, we propose a new HSI classification approach, which is called fused 3-D spectral-spatial deep neural networks for hyperspectral image classification. We propose an unsupervised band selection method to avoid the problem of redundancy between spectral bands and automatically find a set of groups C_k each one containing similar spectral bands. Moreover, the model uses the different groups of selected bands to extract spectral-spatial features in order to improve the classification rate. Each group is associated with a 3-D CNN model, which are then fused to improve the precision of classification. The …