中文摘要：

潮间带表层沉积物粒度参数不仅是研究现代海岸动态和变迁的参照,也是评价人类活动可持续发展的依据。本文以灵山湾潮间带野外实地采集潮滩表层沉积物及其表面实时光谱信息为研究对象,分析高光谱用于粒度参数识别的能力。针对高光谱信息提取的困难,对光谱数据进行数学变换,利用多层感知器神经网络建立沉积物粒度参数与光谱反射率之间的关系模型,探索了利用表层沉积物反射光谱特性进行沉积物特征定量反演的可行性。结果表明：基于高光谱对沉积物粒度参数的定量预测是可行的,建立的预测模型可以较准确地反映潮间带沉积物粒度特征,其快速、准确的优点为测定潮间带表层沉积物粒度参数提供了一种新方法。该模型的建立为进一步提高潮间带变化监测和预测的研究提供了参考借鉴。

英文摘要：

The sediment grain-size parameters of intertidal zone is not only a reference object for the research of modern coastal dynamics but also a basis for the evaluation of the sustainable development of human activities. This paper takes the surface sediment and the hyperspectral information of the intertidal zone in Lingshan Bay as study objects to analyze the ability in grain size parameters identifying. Mathematical transformations were taken aiming at solving the extraction difficulties in hyperspectral data. Multi-layer perceptron ANN （Artificial Neural Network） was established to build a relation model between the parameters and spectral reflectance to explore the feasibility of quantitative inversion of sediment characteristics. The final results show that using hyperspectral data to determine sediment grain-size parameters of intertidal zone is feasible. The advantages, i.e. fast and accurate, provide a new method for determining the intertidal zone sediment parameters. This would provide a reference for accurately monitoring and researching on the changes of intertidal zone.