摘要
局部几何结构Fisher分析通过数据的邻域和邻域的重构来表征高光谱数据的内在流形,可以提升高光谱图像的分类效果。但是该方法使用原始样本点与重构点一起构图,在低维空间上不能有效保持流形的整体结构。针对上述问题,提出了一种局部重构Fisher分析方法;该方法首先使用类内近邻样本重构原始样本,以保持流形的整体结构,然后利用重构点构造本征图和惩罚图。在低维空间中,通过减小类内样本间的距离,增大非同类样本的距离,提高了同类地物的紧凑性和不同类地物的离散性,获得了更好的鉴别特征,有效改善了高光谱图像的分类性能。在Pavia University数据集和Urban数据集上的实验结果表明,相比其他流形学习方法,所提法获得了更高的分类精度。在Pavia University数据集和Urban数据集中随机选取1%的训练样本时,所提方法的总体分类精度相比局部几何结构Fisher分析分别提升了7.84个百分点和1.27个百分点,总体分类精度达到了86.07%和83.77%。
Local geometric structure Fisher analysis(LGSFA)utilizes neighbor points and corresponding reconstructions to determine the intrinsic manifold structure of hyperspectral data,and it can improve the classification accuracy of hyperspectral image(HSI).However,LGSFA uses original sample and reconstruction points to construct graphs together,which cannot effectively preserve the global structure of nonlinear manifolds in low-dimensional spaces.To address this issue,this paper proposes a local reconstruction Fisher analysis(LRFA)method for HSI classification.The proposed method first reconstructs each data point from its intraclass neighbors to learn the global structure of manifolds.Then,intrinsic graph and penalty graph are constructed based on these reconstructions.In the low-dimensional space,the intraclass compactness and the interclass separability are improved by minimizing the intraclass distance and maximizing the interclass distance,respectively.Thus,the distinction in features is enhanced for HSI classification.Experimental results on the Pavia University and Urban datasets prove the effectiveness of the proposed method.Compared with other state-of-art methods,the proposed method achieves higher classification accuracy.When 1%of samples are randomly selected for training,the overall accuracy of 86.07%and 83.77%are obtained and increased by 7.84 percentage points and 1.27 percentage points,respectively,in comparison with the results of LGSFA.
作者
刘嘉敏
杨松
黄鸿
Liu Jiamin;Yang Song;Huang Hong(Key Laboratory of Optoelectronic Technique System of the Ministry of Education,College of Optoelectronic Engineering,Chongqing University,Chongqing 400044,China)
出处
《中国激光》
EI
CAS
CSCD
北大核心
2020年第7期382-393,共12页
Chinese Journal of Lasers
基金
重庆市基础与前沿研究计划(cstc2018jcyjAX0093)
重庆市留学人员回国创业创新支持计划(cx2019144)
重庆市教委科学技术研究计划(KJZD-K20190250).
关键词
遥感
高光谱图像
特征提取
流形学习
图嵌入
局部重构
remote sensing
hyperspectral image
feature extraction
manifold learning
graph embedding
local reconstruction
作者简介
刘嘉敏,E-mail:liujm@cqu.edu.cn。
