在核函数的基础上采用向量扩展的方法改进传统的LMBP算法,将输入向量由低维转换到高维,充分利用误差函数的一二阶导数信息,同时结合传统LMBP算法的优点提高网络训练的收敛速度。仿真实验结果表明,改进方法网络训练的迭代次数更少,分类精度更高,对遥感图像分类更有效。

This paper improves the traditional LMBP algorithm of remote sensing image classification by extending input vectors which have been changed from low dimension to high dimension with kernel function for full utilization of the first and second derivatives information of error function. Simultaneously, combined with the traditional advantages of the LMBP algorithm, it can accelerate the convergence of network training. The simulation results show that the improved method can be more effective in remote sensing image classification because it needs less iteration for network training and achieves higher classification accuracy.