高光谱图像分类的流形学习和非负矩阵分解特征降维研究

项目名称： 高光谱图像分类的流形学习和非负矩阵分解特征降维研究

项目编号： No.61301196

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 无线电电子学、电信技术

项目作者： 温金环

作者单位： 西北工业大学

项目金额： 25万元

中文摘要： 为消除非线性、训练样本不足、特征空间维数过高、波段间强相关性这些因素对高光谱图像分类的不利影响，本项目研究适合高光谱图像分类的流形学习和流形正则NMF特征降维算法，以揭示高光谱数据的流形分布，增强特征提取有效性，提高分类精度。 项目的创新之处：发展融入空间信息的非线性流形学习特征降维理论与方法，可处理大尺度高光谱数据；发展基于线性图嵌入模型的、不需要PCA预处理的线性流形学习特征降维理论与方法，实现了小样本情况下的高光谱图像特征降维；发展基于稀疏模型的流形学习特征降维理论与方法，避免了模型参数的调节，更利于实际应用；基于流形标准和数据非负分解标准发展同时考虑数据几何结构及其判别信息的快速优化流形正则稀疏NMF及流形正则PNMF理论与方法，提高了收敛速度，降低了计算复杂度低，更适合高光谱这样的高维数据。 本项目为高光谱图像特征降维引入了新的研究思路和方法，丰富发展了高光谱图像特征降维的理论。

中文关键词： 流形学习；非负矩阵分解；特征提取；降维；高光谱图像

英文摘要： Hyperspectral image classification was greatly affected by the nonlinear structure of hyperspectral image data, small number of training samples, high dimensionality of feature space, and strong inter-band relativity. In order to eliminate these influences on hyperspectral image classification, this project aims at studying feature dimensionality reduction by manifold learning and manifold regularized nonnegative matrix factorization to reveal the manifold distribution of hyperspectral image data, enhance the efficiency of feature extraction, and improve the classification accuracy. The novelties of this project are the following: Firstly, we will develop the nonlinear manifold learning method which not only integrate the spatial information into feature extraction step but also can handle large scale hyperspectral image data. Secondly, based on linear graph embedding, we will study the linear manifold learning feature extraction method which suit small-sample-size problem without the PCA preprocessing step. Thirdly, we will develop manifold learning theory and method based on sparse representation which avoid adjusting the model parameters and is more conducive to the practical application. Finally, based on manifold criterion and nonnegative factorization criterion, we will study manifold regularized sparse n

英文关键词： manifold learning；non-negative matrix factorization；feature extraction；dimensionality reduction；hyperspectral image