摘要
高光谱激光雷达数据在频谱维度上具有很高的维度,包含大量的波段或频带,易出现忽视频谱带中有用信息的情况,进而导致高光谱激光雷达信号分选效果较差。为此,提出基于改进随机森林的高光谱激光雷达信号分选研究。首先,采用变分模态分解算法对高光谱激光雷达含噪信号展开去噪处理;然后,采用长短期记忆神经网络算法对去噪后的高光谱激光雷达信号展开特征提取,并利用自编码神经网络对提取的特征展开重构处理,以获取重构后的雷达信号特征;最后,采用随机森林算法根据高光谱激光雷达信号特征完成信号分选。实验结果表明,所提方法的SNR为30.648 dB,RMSE为0.1498,预测分选类别与实际分选类别几乎一致,分析时间始终未超过5 s,表明所提方法的分选性能较好,具有实用性。
The high dimensionality of hyperspectral LiDAR data in the spectral dimension,which includes a large number of bands or frequency bands,it is easy to overlook useful information in the video spectral band,resulting in poor signal sorting performance of hyperspectral LiDAR.Therefore,a study on hyperspectral LiDAR signal sorting based on improved random forest is proposed.Firstly,the variational modal decomposition algorithm is used to denoise the noisy signal of hyperspectral lidar;Then,a long and short term memory neural network algorithm is used to extract features from the denoised hyperspectral LiDAR signal,and a self coding neural network is used to reconstruct the extracted features to obtain the reconstructed radar signal features;Finally,the random forest algorithm is used to complete signal sorting based on the characteristics of hyperspectral LiDAR signals.The experimental results show that the SNR of the proposed method is 30.648 dB,and the RMSE is 0.1498.The predicted sorting category is almost consistent with the actual sorting category,and the analysis time does not exceed 5 s,indicating that the proposed method has good sorting performance and practicality.
作者
刘子恒
刘汉城
敏乾
LIU Ziheng;LIU Hancheng;MIN Qian(Gansu Normal College for Nationalities,Hezuo Gansu 747000,China)
出处
《激光杂志》
CAS
北大核心
2024年第8期218-223,共6页
Laser Journal
基金
甘肃省自然科学基金项目(No.23JRRP0002)。
关键词
高光谱激光雷达信号
随机森林
变分模态分解算法
长短期记忆神经网络算法
自编码神经网络
hyperspectral LiDAR signal
random forest
variational modal decomposition algorithm
long and short term memory neural network algorithm
self coding neural network
