Unsupervised hyperspectral images classification using hypergraph convolutional extreme learning machines

Abstract

AbstractAiming at the problem that traditional methods are difficult to fully utilize the rich spectral information in hyperspectral images (HSI) and fail to capture the complex higher‐order relations in hyperspectral data, which leads to limited classification performance extreme learning machine and fails to further improve the classification accuracy of HSIs, the authors propose the hypergraph convolutional extreme learning machine (HGCELM) method. The method not only inherits all the advantages of extreme learning machine (ELM), but also embeds hypergraph convolution for feature selection, which is capable of handling higher‐order relations. This enables HGCELM to capture more complex relationships between nodes and provide richer representation capabilities. At the same time, the training speed advantage of ELM is retained, thus speeding up the model training process. Experimental results show that the proposed algorithm achieves better accuracy compared to other clustering algorithms.