One superpixel can be regarded as a small region consisting of a number of pixels with similar spectral characteristics. The novel framework utilizes superpixel to exploit spatial information which can improve classification accuracy. Specifically, we first adopt an efficient segmentation algorithm to divide the HSI into many superpixels. Then, spatial features of superpixels are extracted by computing the mean of the spectral pixels within each superpixel. The mean feature can combine the spatial and spectral information of each superpixel. Finally, ELMs is used for the classification of each mean feature to determine the class label of each superpixel.
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In SaE-ELM, the network hidden node parameters are optimized by the self-adaptive differential evolution algorithm, whose trial vector generation strategies and their associated control parameters are self-adapted in a strategy pool by learning from their previous experiences in generating promising solutions, and the network output weights are calculated using the Moore—Penrose generalized inverse. SaE-ELM outperforms the evolutionary extreme learning machine E-ELM and the different evolutionary Levenberg—Marquardt method in general as it could self-adaptively determine the suitable control parameters and generation strategies involved in DE. Simulations have shown that SaE-ELM not only performs better than E-ELM with several manually choosing generation strategies and control parameters but also obtains better generalization performances than several related methods. This is a preview of subscription content, log in to check access. Access options Instant access to the full article PDF. Subscription will auto renew annually.