基于遗传算法优化的BP神经网络遥感数据土地覆盖分类

    Optimized BP neural network classifier based on genetic algorithm for land cover classification using remotely-sensed data

    • 摘要: 提出了一种基于遗传算法优化的BP神经网络遥感分类方法。该方法兼顾了遗传算法和梯度下降优化算法分别在全局和局部搜索极小点的优势;避免了在BP网络训练过程中过早收敛于局部极小点的风险;与BP算法相比,该算法多次重复过程所得网络的均方差比较稳定。在算法验证中,用中巴地球资源一号卫星数据作为试验数据,详细描述了网络优化过程中的参数设置和关键参数变化过程,比较了该算法与BP算法、最大似然法的分类精度。分类试验表明:该算法不但有较高的执行效率,也能达到很高的分类精度。

       

      Abstract: A new Error Back Propagation algorithm based Genetic Algorithm was proposed in the article, and the key steps and framework were described in detail. The algorithm gives attention to two optimization algorithms, genetic algorithm and back propagation algorithm, which have the advantage during searching the infinitesimal point in local space and global space respectively, avoiding the risk of premature convergence while BP network was training; compared with BP algorithm, the end total mean square error of the network is more stable even if people redo the whole course several times. The data from China-Brazil Earth Resources Satellite were used to validate the algorithm; meanwhile the setting parameters and change processing of parameter were depicted carefully. Maximum likelihood classifier, back propagation neural network classifier were involved for a comparison purpose. The experiment results show that the new algorithm cannot only run with better efficiency, but also achieve the best classification accuracy.

       

    /

    返回文章
    返回