根据赤潮的卫星遥感探测机理，应用人工神经网络技术，建立和利用NOAA AVHRR可见光和热红外波段遥感数据的BP神经网络赤潮信息提取模型。应用实例显示。基于该人工神经网络方法可以提取赤潮发生地点和范围等信息，赤潮探测正确率达到78.5%。研究结果表明，应用人工神经网络方法提取赤潮信息是可行的。本文中建立的BP赤潮信息提取模型适当修改后可移植应用于其它传感器遥感数据进行赤潮信息提取。

The Advanced Very High Resolution Radiometer (AVHRR)on board the TIROS-N/NOAA series of operational meteorological satellites has both visible and thermal infrared channels,which enable sea surface temperatures (SSTs) and other parameters related to water quality to be derived. An artificial neural network (ANN) method for detecting red tides with AVHRR imagery has been developed in this paper. The detection of red tides is based on the fact that 1) the seawater has higher concentration of phytoplankton pigments when red tides occur and 2) the occurrence of red tides in associated with sea surface temperature. The ANN method uses reflectivity in AVHRR visible channels and SSTs derived from AVHRR thermal infrared channels as inputs with five nodes in a single hidden layer to model the nonlinear transfer function between red tides and AVHRR data. The ANN method has been trained and tested using in situ and airborne measurements. The ANN method has been applied to detect red tide events occurred in the Bohai Sea of China in 1999.The results have illustrated good performance of the ANN method with a detection accuracy of 78.5%.