A Spectral-Spatial Domain-Specific Convolutional Deep Extreme Learning Machine for Supervised Hyperspectral Image Classification

Spectral-spatial feature extraction is of great importance to hyperspectral image (HSI) classification. Different from the traditional feature extraction methods, deep learning models such as convolutional neural network (CNN) can learn the spectral-spatial discriminative feature automatically. However, deep learning models usually need to construct a large and complicated network and the training is time-consuming. To deal with these issues, in this paper, a spectral-spatial domain-specific convolutional deep extreme learning machine (ELM), named S2CDELM, is proposed for HSI classification. At first, by using the conception of local receptive filed (LRF), a spectral-spatial convolutional learning module with two branches is constructed for spectral and spatial feature extraction respectively. Specifically, the convolutional learning module is constructed by using random convolutional nodes but without back propagation, in which a spectral branch and a spatial branch are designed respectively. Then the extracted features are concatenated and fed to a fully connected stacked ELM network to further exploit spectral-spatial information for classification. As the convolutional filters and input weights of ELM are randomly generated, the whole framework is compact, simple and fast to construct. Experimental results on popular HSI benchmark data sets demonstrate that S2CDELM can provide satisfactory classification performance and a fast learning speed in comparison with several state-of-the-art classifiers.