西南交通大学学报Issue(2)：336-341,6.DOI:10.3969/j.issn.0258-2724.2015.02.020
基于结构性字典学习的高光谱遥感图像分类
Hyperspectral Image Classification Based on Structured Dictionary Learning
摘要
Abstract
In order to improve the classification accuracy of hyperspectral images,a new structured dictionary-based method for hyperspectral image classification was proposed. This method incorporates both spatial and spectral characteristics of hyperspectral images to obtain a dictionary of each pixel,the pixels in an identical pixel group have a common sparsity pattern;image sparsity representation coefficients are calculated in light of the dictionary to gain sparse representation features of hyperspectral images;the classification of hyperspectral images is determined using a linear support vector machine. Experiments on AVIRIS and ROSIS hyperspectral images were carried out. The experimental results show that compared with the common dictionary learning,the classification accuracy is respectively raised by 0 . 041 1 and 0 . 046 6 ,the Kappa coefficient is improved by 0 . 179 3 and 0. 056 3,respectively.关键词
高光谱遥感图像/结构性字典学习/支持向量机/分类Key words
hyperspectral image/structured dictionary learning/support vector machine/classification分类
信息技术与安全科学引用本文复制引用
秦振涛,杨武年,杨茹,潘佩芬,邓琮..基于结构性字典学习的高光谱遥感图像分类[J].西南交通大学学报,2015,(2):336-341,6.基金项目
国家自然科学基金资助项目（41071265，41372340）；高等学校博士学科点专项科研基金资助项目（20105122110006）；国土资源部地学空间信息技术重点实验室开放基金资助项目 （）
