The Monitoring of Red Tides Based on Modular Neural Networks Using Airborne Hyperspectral Remote Sensing

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.

这篇论文建议基于聚类和模块化的神经网络监视方法的红潮。从天线的一个团获得红潮的特征遥远的 sensinghyperspectral 数据，首先，日志剩余修正(纵向冗余码校验) 被用来使数据，然后聚类的分析正常化被采用为神经网络选择并且形成训练样本。监视的 Forrapid，辨别者由模块化的神经网络组成，其结构和学习参数被一个适应基因算法(统帅) 决定。实验证明这个方法罐头很快并且有效地监视红潮。