Abstract
We propose an efficient framework for hyperspectral image (HSI) classification based on superpixel and extreme learning machines (ELMs). 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. Experiments on two real HSIs demonstrate the outstanding performance of the proposed method in terms of classification accuracies and high computational efficiency.
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Duan, W., Li, S., Fang, L. (2014). Spectral-Spatial Hyperspectral Image Classification Using Superpixel and Extreme Learning Machines. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_17
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DOI: https://doi.org/10.1007/978-3-662-45646-0_17
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