HSI-GCN: hyperspectral image classification algorithm based on Gabor convolutional networks

Purpose Hyperspectral imaging (HSI) systems are becoming potent technologies for computer vision tasks due to the rich information they uncover, where each substance exhibits a distinct spectral distribution. Although the high spectral dimensionality of the data empowers feature learning, the joint spatial–spectral features have not been well explored yet. Gabor convolutional networks (GCNs) incorporate Gabor filters into a deep convolutional neural network (CNN) to extract discriminative features of different orientations and frequencies. To the best if the authors’ knowledge, this paper introduces the exploitation of GCNs for hyperspectral image classification (HSI-GCN) for the first time. HSI-GCN is able to extract deep joint spatial–spectral features more rapidly and accurately despite the shortage of training samples. The authors thoroughly evaluate the effectiveness of used method on different hyperspectral data sets, where promising results and high classification accuracy have been achieved compared to the previously proposed CNN-based and Gabor-based methods. Design/methodology/approach The authors have implemented the new algorithm of Gabor convolution network on the hyperspectral images for classification purposes. Findings Implementing the new GCN has shown unexpectable results with an excellent classification accuracy. Originality/value To the best of the authors’ knowledge, this work is the first one that implements this approach.