摘要
高光谱图像的数据维数高、数据量大、数据间高度冗余等特点给图像分类带来困难,为进行有效降维、提高分类精度,提出了一种监督局部线性嵌入(SLLE)非线性流形学习特征提取方法。SLLE算法根据数据先验类标签信息所给出的新距离寻找数据点的k最近邻(NN),新距离使得类内距离小于类间距离,这使得SLLE算法更有利于分类。高光谱图像数据和UCI数据的分类结果表明了该方法的有效性。
Hyperspectral image has high spectral dimension, vast data and altitudinal interband redundancy, which brings problems to image classification. To effectively reduce dimensionality and improve classification precision, a new extraction method of nonlinear manifold learning feature based on Supervised Local Linear Embedding （SLLE） for classification of hyperspectral image was proposed in this paper. A data point＇s k Nearest Neighbours （NN） were found by using new distance function which was proposed according to prior class-label information. Because the intra-class distance is smaller than inter-class distance, classification is easy for SLLE algorithm. The experimental results on hyperspectral datasets and UCI data set demonstrate the effectiveness of the presented method.
出处
《计算机应用》
CSCD
北大核心
2011年第3期715-717,720,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(10926197
60972150)
西北工业大学基础研究基金资助项目(JC201053)
关键词
特征提取
降维
监督局部线性嵌入
流形学习
高光谱图像分类
feature extraction
dimensionality reduction
Supervised Locally Linear Embedding （SLLE）
manifold learning
hyperspectral image classification
