基于BP神经网络的盐渍土盐分遥感反演模型研究

Research on Salinity Inversion from Remote Sensing of Saline Soil Based on BP Neural Network

采用遥感技术和BP神经网络技术,结合野外实测的盐渍土光谱特征和实验室化验的土壤含盐数据,对盐渍土盐分的遥感反演进行了模型的设计与编程实现。BP神经网络模型的预测精度在62.5%,明显高于传统统计模型的预测精度,表明BP神经网络能较好地模拟土壤含盐量与光谱数据之间的关系,可用于建立土壤盐分遥感反演模型。

The research on salinity inversion from remote sensing with the measured spectral data and salinity data has been done using remote sensing technology and BP neural network technology. The model was designed and implemented by pro- gramming. The forecast accuracy of BP neural network model is 62.5 %,better than statistical model. The experiment confirms that BP neural network can simulate the relationship between soil salinity and spectral data, which shows that it is feasible to use this method in salinity inversion from remote sensing.