低秩稀疏和改进SAM的高光谱图像误标签检测  

False label detection in hyperspectral image based on low rank sparse and improved SAM

作　　者：刘煊 渠慎明[1,2] LIU Xuan;QU Shenming(School of Software,Henan University,Kaifeng 475001,China;Institute of Intelligence Networks System,Henan University,Kaifeng 475001,China)

机构地区：[1]河南大学软件学院,开封475001 [2]河南大学智能网络系统研究所,开封475001

出　　处：《激光技术》2022年第6期808-816,共9页Laser Technology

基　　金：河南省科技发展计划资助项目(212102210538);河南大学研究生教育创新与质量提升计划资助项目(SYL20040121)。

摘　　要：为了解决基于监督学习的高光谱图像分类算法训练样本中存在的噪声标签会降低后续的分类精度的问题,采用了一种基于低秩稀疏表示和改进光谱角制图(SAM)的高光谱图像误标签检测算法。首先对高光谱图像中信号子空间进行预测,根据预测到的子空间对原始高光谱图像重构并去噪;然后通过基于归一化的光谱角制图算法来获取每一类样本间的距离信息,得到每类样本间的光谱相似度,并利用密度峰值聚类算法得到每个训练样本的局部密度;最后采用基于局部密度的决策函数对噪声标签进行检测,使用支持向量机在两个真实数据集上验证。结果表明,该算法比先进的层次结构的高光谱图像误标签检测算法提高了1.91%的总体精度。这一结果对高光谱图像分类是有帮助的。In order to solve the problem that reduction of the subsequent classification accuracy in the hyperspectral image classification algorithm based on supervised learning due to the presence of noise labels in the training samples, a false label detection algorithm based on low rank sparse representation and improved spectral angle mapping(SAM) was adopted. Firstly, the signal subspace of hyperspectral image was predicted, and the original hyperspectral image was reconstructed and denoised according to the predicted subspace. Next, the normalized spectral angle mapping algorithm was used to obtain the distance information between each class of samples, and the spectral similarity between each class of samples was obtained. Then, the density peak clustering algorithm was used to get the local density of each training sample. Support vector machine was used to verify the results on two real datasets. The experimental results show that the overall accuracy is improved by 1.91% compared with the advanced hierarchical structure of hyperspectral image false label detection algorithm. This result is helpful for hyperspectral image classification.

关 键 词：图像处理 低秩稀疏表示 归一化光谱角制图 密度峰值聚类算法 噪声标签检测 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]