MEA-BP模型在遥感影像分类中的应用研究

遥感影像分类作为遥感技术的一个重要应用,对遥感技术的发展具有重要作用.针对遥感影像数据特点,在目前的非线性研究方法中主要用到的是BP神经网络模型.但是BP神经网络模型存在对初始权阈值敏感、易陷入局部极小值和收敛速度慢的问题.因此,为了提高模型遥感影像分类精度,提出采用MEA-BP模型进行遥感影像数据分类.首先采用思维进化算法代替BP神经网络算法进行初始寻优,再用改进BP算法对优化的网络模型权阈值进一步精确优化,随后建立基于思维进化算法的BP神经网络分类模型,并将其应用到遥感影像数据分类研究中.仿真结果表明,新模型有效提高了遥感影像分类准确性,为遥感影像分类提出了一种新的方法,具有广泛研究价值.

Research on the Application of MEA-BP Model in Remote Sensing Image Classification

Remote sensing image classification is an important application of remote sensing technology,plays an important role in the development of remote sensing technology.In view of the characteristics of remote sensing image data,the BP neural network model is used in the current nonlinear research methods.But the BP neural network model is sensitive to the initial weight threshold,easy to fall into local extremum and slow convergence speed.Therefore,in order to improve the classification accuracy of the model,the MEA-BP model is proposed to classify the remote sensing image data.First the use of mind evolutionary algorithm instead of BP neural network algorithm for the initial optimization.Then,using the modified BP algorithm to optimize the network model of threshold further accurate optimization,then established based on mind evolutionary algorithm of BP neural network classification model,and its application to the classification research of remote sensing image data.The simulation results show that the new model can effectively improve the accuracy of remote sensing image classification,and put forward a new method for the classification of remote sensing image,which has a wide range of research value.