计算机工程 ›› 2019, Vol. 45 ›› Issue (1): 246-252.doi: 10.19678/j.issn.1000-3428.0049037

• 图形图像处理 • 上一篇    下一篇

基于独立空谱残差融合的联合稀疏表示高光谱图像分类

卢佳,保文星   

  1. 北方民族大学 计算机科学与工程学院,银川 750021
  • 收稿日期:2017-10-23 出版日期:2019-01-15 发布日期:2019-01-15
  • 作者简介:卢佳(1992—),女,硕士,主研方向为遥感图像处理与应用;保文星(通信作者),教授。
  • 基金项目:

    国家自然科学基金“基于遥感的宁夏工业固体废物环境监测研究”(61461003);北方民族大学校级创新项目(YCX1756)。

Joint Sparse Representation Hyperspectral Image Classification Based on Independent Space-spectrum Residual Fusion

LU Jia,BAO Wenxing   

  1. School of Computer Science and Engineering,North Minzu University,Yinchuan 750021,China
  • Received:2017-10-23 Online:2019-01-15 Published:2019-01-15

摘要:

针对高光谱图像分类中存在的空间信息与光谱信息融合问题,提出一种基于独立空谱残差融合的联合稀疏表示高光谱图像分类算法。使用类独立的光谱角初分类图像,获得像元初始标记后按特定条件进行筛选再构造像元邻域空间。提取图像的全局空间信息,并将其引入到空谱联合稀疏表示模型中,使用单独的光谱信息字典与空间信息字典分别进行图像光谱与空间的联合稀疏表示残差计算。在此基础上,使用残差融合算法确定图像类别。实验结果表明,相对SVM、KNN等算法,该算法能够提升高光谱图像的分类精度,且分类结果更稳定。

关键词: 高光谱图像, 联合稀疏表示, 全局空间信息, 光谱信息, 残差融合

Abstract:

Aiming at the problem of spatial information and spectral information fusion in hyperspectral image classification,a joint sparse representation hyperspectral image classification algorithm based on independent space-spectrum residual fusion is proposed.Class-independent spectral angles are used to classify the images,and the initial labels of pixels are obtained,then the neighborhood space of pixels is constructed according to the specific conditions.The global spatial information of the whole image is extracted and introduced into the joint sparse representation model of space-spectrum.The joint sparse representation residual of image spectrum and space is calculated by using a separate spectral information dictionary and a spatial information dictionary respectively.On this basis,the residual fusion algorithm is used to determine image classes.Experimental results show that compared with SVM,KNN and other algorithms,this algorithm can improve the classification accuracy of spectral images and the classification results are more stable.

Key words: hyperspectral image, joint sparse representation, global spatial information, spectral information, residual fusion

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