摘要
提出了两种基于Gabor特征与局部保护降维的高光谱图像分类算法。该算法利用嵌入主成分分析的Gabor变换对高光谱图像进行特征提取。为了保护相邻特征的局部信息,利用局部Fisher判别分析或局部保护非负矩阵分离对Gabor特征进行降维,并采用高斯混合模型分类器对降维后的特征进行分类。两组高光谱数据的实验结果表明,本文算法不但能充分挖掘高光谱图像的谱间-空间特征,而且有效保护了高光谱图像的局部特征信息与多模型结构。与现有的几种算法相比,本文算法能得到更高的分类精度和Kappa系数,在高斯噪声环境中也具有更强的稳健性。
Two hyperspectral image classification algorithms based on Gabor features and locality-preserving dimensionality reduction are proposed. The Gabor transform is studied and implemented to extract features for hyperspectral image in the principal component analysis-projected domain. To protect locality information of neighbor features, locality Fisher discriminant analysis or locality-preserving non-negative matrix factorization is employed to reduce the dimensionality of Gabor-based feature space. The Gaussian mixture model classifier is used for classification results. Experimental results obtained from two hyperspectral datasets show that the proposed algorithms not only extract spectral-spatial features effectively, but also preserve local-feature information and multi- model structure of hyperspectral image. Compared with several existing algorithms, the proposed algorithms can obtain high classification accuracy and Kappa coefficient, and has strong robustness in Gaussian noise environment.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2016年第10期504-513,共10页
Acta Optica Sinica
基金
国家自然科学基金(41201363
41601344)
中央高校基本科研业务费专项资金(310832163402
310832161001)
关键词
遥感
高光谱图像分类
GABOR特征
局部保护降维
高斯混合模型
remote sensing
hyperspectral image classification
Gabor features
locality-preserving dimensionality reduction
Gaussian mixture model
