Abstract
This paper proposes a red tide monitoring method based on clustering and modular neural networks. To obtain the features of red tide from a mass of aerial remote sensing hyperspectral data, first the Log Residual Correction (LRC) is used to normalize the data, and then clustering analysis is adopted to select and form the training samples for the neural networks. For rapid monitoring, the discriminator is composed of modular neural networks, whose structure and learning parameters are determined by an Adaptive Genetic Algorithm (AGA). The experiments showed that this method can monitor red tide rapidly and effectively.
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Guangrong, J., Jie, S., Wencang, Z. et al. The monitoring of red tides based on modular neural networks using airborne hyperspectral remote sensing. J. Ocean Univ. China 5, 169–173 (2006). https://doi.org/10.1007/BF02919218
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DOI: https://doi.org/10.1007/BF02919218