AGA-BP模型在遥感影像分类中的应用研究

作为遥感研究的关键技术,遥感影像分类一直是遥感研究热点;针对目前采用BP神经网络模型进行遥感影像分类时存在的对初始权阈值敏感、易陷入局部极值和收敛速度慢的问题,为了提高BP模型遥感影像分类精度,将自适应遗传算法引入到BP网络模型参数选择中;首先运用自适应遗传算法对BP模型权阈值参数进行初始寻优,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,随后建立基于自适应遗传算法的BP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。

Application of AGA-BP Model in Remote Sensing Image Classification

as the key technology of remote sensing, remote sensing image classification has been a hot spot of remote sensing research. In view of the current BP neural network model for remote sensing image classification, it is sensitive to the initial weight threshold, easy to fall into local extreme value and slow convergence speed, in order to improve the classification accuracy of BP model, the adaptive genetic algorithm is introduced into the parameter selection of BP network model. Firstly, the adaptive genetic algorithm is used to optimize the parameters of BP model weight threshold, then the improved BP algorithm is used to optimize the weight threshold of the optimized network, subsequently, a classification model of BP network based on adaptive genetic algorithm is established, and it is applied to the classification 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.