Contextual Online Dictionary Learning for Hyperspectral Image Classification

Wei Fu, Shutao Li*, Leyuan Fang, Jon Atli Benediktsson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

Abstract

Sparse representation (SR) has been successfully used in the classification of hyperspectral images (HSIs) by representing HSI pixels over a dictionary and yielding discriminative sparse coefficients. Most of SR-based classification methods construct the dictionary by directly using some labeled pixels as atoms. Such dictionary can lead to inefficient SR for large-sized HSIs, and may be incomplete when the number of labeled pixels is less than the number of spectral bands. This paper proposes a contextual online dictionary learning (DL) method for HSIs classification, which learns a dictionary over the whole image rather than few labeled pixels. The proposed method can effectively and efficiently improve the adaptive representation capability of different pixels with an online learning mechanism. Specifically, the contextual characteristics of the HSI are integrated with discriminative spectral information for online DL, i.e., pushing similar pixels in neighborhood to share similar sparse coefficients with respect to the well-learned dictionary. By this way, the obtained sparse coefficients are structured and discriminative. Finally, a traditional classifier, i.e., the linear support vector machine, is applied to the sparse coefficients, and the final classification results are obtained. Experimental results on real HSIs show the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)1336-1347
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume56
Issue number3
DOIs
Publication statusPublished - Mar 2018

Bibliographical note

Funding Information:
Dr. Fang received the Scholarship Award for Excellent Doctoral Student granted by the Chinese Ministry of Education in 2011.

Funding Information:
Manuscript received March 21, 2017; revised July 17, 2017 and September 21, 2017; accepted October 6, 2017. Date of publication October 27, 2017; date of current version February 27, 2018. This work was supported by the National Natural Science Fund of China for Distinguished Young Scholars under Grant 61325007, by the National Natural Science Fund of China for International Cooperation and Exchanges under Grant 61520106001, and by the Fund of Hunan Province for Science and Technology Plan Project under Grant 2017RS3024. (Corresponding author: Shutao Li.) W. Fu, S. Li, and L. Fang are with the College of Electrical and Information Engineering, Hunan University, Changsha 410082, China (e-mail: fuweihunandaxue@gmail.com; shutao_li@hnu.edu.cn; fangleyuan@gmail.com).

Publisher Copyright:
© 1980-2012 IEEE.

Other keywords

  • Classification
  • Contextual characteristics
  • Hyperspectral images (HSIs)
  • Online dictionary learning (DL)
  • Sparse representation (SR)

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