Morphologically dilated convolutional neural network for hyperspectral image classification

Abstract The use of hyperspectral images is expanding rapidly with the advancement of remote sensing technologies. The precise classification of features for mapping land cover through hyperspectral images is a major research topic and a major focus. In the classification of hyperspectral images, several methods provided good classification results. Among all, convolutional neural network (CNN) is a widely used deep neural network due to its robust feature extraction capabilities. It can enhance the hyperspectral image classification accuracy. Mathematical morphology (MM) is a robust and straightforward spatial feature descriptor, which can reduce the computational workload. We proposed a novel model morphologically dilated Convolutional Neural Network (MDCNN), which can extract more robust spectral–spatial features. MDCNN adopt a process to concatenate the morphological feature maps with original hyperspectral data. CNN structure uses both traditional and dilated convolution. Replacing the dilated convolution in traditional convolution layers expands the receptive field without boosting parameters and thus improves network performance without increasing network complexity. The dilation layer does not reduce the number of parameters but reduces the size of the output feature map, which leads to the overall reduction in the number of parameters. 3D convolution extracts spectral–spatial features and maintains the correlation of spectral data. 2D CNN extracts spatial features and reduces the model’s complexity, which can occur if only 3D convolution is used. Experimental findings show that the proposed approach can provide better classification results than traditional deep learning models and other state-of-the-art models on the Indian Pines, University of Pavia, and Salinas Scene data.