地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (2): 263-271.doi: 10.3724/SP.J.1047.2016.00263

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光谱与空间维双重稀疏表达的高光谱影像分类

朱勇, 吴波*()   

  1. 福州大学 空间数据挖掘与信息共享教育部重点实验室,福建省空间信息工程研究中心,福州 350002
  • 收稿日期:2015-05-11 修回日期:2015-10-27 出版日期:2016-02-10 发布日期:2016-02-04
  • 通讯作者: 吴波 E-mail:zhuyongfz@126.com;wavelet778@sohu.com
  • 作者简介:

    作者简介:朱 勇(1989-),男,硕士,研究方向为遥感图像处理。E-mail: zhuyongfz@126.com

  • 基金资助:
    基金项目:福建省自然科学基金项目“基于结构化稀疏表达模型的遥感影像时空融合方法研究”(2015J01163);国家自然科学基金项目“基于稀疏转换学习的遥感影像时空融合模型与方法研究”(41571330);国家科技支撑计划项目(2013BAC08B01)

Classification of Hyperspectral Images with Spectral-Spatial Sparse Representation

ZHU Yong(), WU Bo*()   

  1. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350002, China
  • Received:2015-05-11 Revised:2015-10-27 Online:2016-02-10 Published:2016-02-04
  • Contact: WU Bo E-mail:zhuyongfz@126.com;wavelet778@sohu.com

摘要:

高光谱遥感影像的稀疏分类是当前遥感信息处理的研究热点。本文提出一种光谱与空间双重稀疏表达的高光谱遥感影像分类方法(WSSRC)。首先利用小波字典对光谱维进行稀疏表示,将光谱维稀疏分类转化到小波域稀疏分类;其次,考虑空间邻域地物光谱的统一性和差异性,对邻域内像元分别进行稀疏编码,并对编码进行累加聚合;然后,利用聚合后的稀疏编码构造线性分类器对高光谱影像进行分类;最后,通过2幅标准的高光谱影像数据验证了本文所提出的方法。实验结果表明,该方法能有效地提高影像的分类精度。

关键词: 小波, 双重稀疏分类, 稀疏编码, 高光谱影像

Abstract:

A novel sparse representation classification model with spectral-spatial sparsity properties is presented to improve the classification accuracy of hyperspectral images. Firstly, this method uses the wavelet dictionary as the core dictionary to extract spectral domain sparse information, and then the spectral dimension sparse representation classification is transformed into the wavelet domain (WSRC) by inverse wavelet transformation. After that, we actually extract the sparse spectral features of the hyperspectral images and increase the recognition of the original dictionary. Secondly, considering the unity and diversity of the spatial adjacent object, we realize the sparse coding of the neighborhood pixels, and then accumulate the sparse codes. At the same time, we classify the hyperspectral images using a linear classifier that is based on the accumulated sparse codes. This method ensures that we extract the main sparse signal of the neighborhood pixels on the basis of the personality features of sparse encoding, and it performs better than the joint sparse representation model (JSRC) which is directly based on the neighborhood pixels. Finally, two commonly used hyperspectral images are utilized to validate the proposed model. The experimental results demonstrate that the proposed algorithm outperforms other models in terms of overall accuracy and kappa coefficient measurements.

Key words: wavelet transform, double sparse representation, sparse code, hyperspectral image