半监督空谱局部判别分析的高光谱影像特征提取  

Semi-supervised spatial spectral local discriminant analysis for hyperspectral image feature extraction

作　　者：吕欢欢[1,2] 黄煜铖 张辉 王雅莉 LU Huanhuan;HUANG Yucheng;ZHANG Hui;WANG Yali(College of Software,Liaoning Technical University,Huludao 125105,China;College of Information Engineering,Huzhou University,Huzhou 313000,China)

机构地区：[1]辽宁工程技术大学软件学院,辽宁葫芦岛125105 [2]湖州师范学院信息工程学院,浙江湖州313000

出　　处：《液晶与显示》2024年第2期131-145,共15页Chinese Journal of Liquid Crystals and Displays

基　　金：浙江省教育厅一般科研项目(No.Y202248546)。

摘　　要：为充分利用高光谱影像中蕴含的空谱特征,提出了一种半监督空谱局部判别分析的高光谱影像特征提取算法(S4LFDA)。鉴于高光谱数据集具有空间一致性,首先将像元进行空间重构,保存高光谱数据的近邻关系;其次引入光谱信息散度重构像元间的相似度;为了充分利用大量无标签样本提高算法性能,采用模糊C均值聚类算法对样本进行聚类分析得到伪标签;然后通过增加规范化项到局部力导引算法(FDA)的类内散度矩阵和类间散度矩阵中,以此保持无标签样本的聚类结构一致性;最后通过局部FDA算法来保持有标签样本类间散度最大化和类内散度最小化并求解最佳投影向量。S4LFDA算法既保持了数据集在光谱域的可分性,又保持了像元在空间区域内的近邻关系,合理利用有标签样本及无标签样本,提高了算法的分类性能。在Pavia University和Indian Pines数据集上进行实验,总体分类精度达到95.60%和94.38%。与其他维数约简算法相比,该算法有效提高了地物分类性能。Making full use of the spatial spectral features contained in hyperspectral images,a hyperspectral image feature extraction algorithm(S4LFDA)for semi-supervised spatial spectral local discriminant analysis is proposed.In view of the spatial consistency of hyperspectral datasets,the pixels are first spatially reconstructed to preserve the neighbor relationship of hyperspectral data,and the spectral information divergence is introduced to reconstruct the similarity between cells.In order to make full use of a large number of unlabeled samples to improve the performance of the algorithm,the fuzzy C-means clustering algorithm is used to cluster the samples to obtain pseudo-labels.Then,the normalization term is added to the intra-class divergence matrix and interclass divergence matrix of the local FDA algorithm to maintain the consistency of the cluster structure of the unlabeled samples.Finally,the local FDA algorithm is used to maximize the interclass divergence and minimize the intra-class divergence of the labeled samples and solve the best projection vector.The S4LFDA algorithm not only maintains the divisibility of the data set in the spectral domain,but also maintains the neighbor relationship of the pixels in the spatial region,rationally uses labeled samples and unlabeled samples,and improves the classification performance of the algorithm.Experiments are carried out in Pavia University and Indian Pines,and the overall classification accuracy reaches to 95.60% and 94.38%,which effectively improves the performance of feature classification compared with other dimensional reduction algorithms.

关 键 词：高光谱影像 半监督 空谱 判别分析 特征提取 地物分类 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]