基于空间光谱联合的LPP算法  

LPP Algorithm Based on Spatial-Spectral Combination

作　　者：邹彦艳[1] 田年年 ZOU Yanyan;TIAN Niannian(School of Electrical and Information Engineering,Northeast Petroleum University,Daqing 163318,China)

机构地区：[1]东北石油大学电气信息工程学院,黑龙江大庆163318

出　　处：《吉林大学学报（信息科学版）》2024年第3期550-558,共9页Journal of Jilin University（Information Science Edition）

摘　　要：针对原始的流形学习算法仅利用其光谱特征而没有利用空间信息的问题,提出了基于监督的空谱联合的局部保持投影算法(SS-LPP:Spatial-Spectral Locality Preserving Projections)。该算法首先使用加权均值滤波算法对数据集进行滤波,将空间信息与光谱信息进行融合并消除噪点的干扰,增加同类数据的相关性。然后利用标签集构造类内图和类间图,并通过其可有效提取鉴别特征和改善分类性能。在Salinas和PaviaU数据集上对该算法的有效性进行验证。实验结果表明,该算法能有效提取数据特征,并提高分类的准确性。Aiming to the problem that the original manifold learning algorithm only utilizes spectral characteristics without incorporating spatial information,a locality preserving projections algorithm based on spatial-spectral(SS-LPP:Spatial-Spectral Locality Preserving Projections)union is proposed.Firstly,the weighted mean filtering algorithm is used to filter the dataset,fuse the spatial information with the spectral information,and eliminate the interference of noise,to increase the smoothness of similar data.Then,the label set is used to construct intra-graph and inter-graph.Through the intra-graph and inter-graph,identification features can be effectively extracted,and the classification performance can be improved.The effectiveness of the algorithm is verified on the Salinas dataset and the PaviaU dataset.Experimental results show that the algorithm can effectively extract data features and improve the accuracy of classification.

关 键 词：流形学习 降维 高光谱遥感影像 特征提取 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]