Supervised Functional Data Discriminant Analysis for Hyperspectral Image Classification

Zhijing Ye, Jiaqing Chen, Hong Li, Yantao Wei*, Guangrun Xiao, Jon Atli Benediktsson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


This article proposes a functional data discriminant analysis (FDDA) method for hyperspectral image (HSI) classification. This method analyzes and processes the HSI data from a functional point of view, which is a novel perspective in HSI processing. The classical methods achieve dimensionality reduction by directly eliminating the redundancy of the HSI data. However, the proposed method extracts the functional features by utilizing the redundancy of the HSI data. Functional features can effectively reveal inherent characteristics of the HSI data with the change in the wavelengths. Based on this, a regularized weighted fitting model is first built for converting a spectral vector into a spectral curve. Second, an FDDA method defined in the function field is presented for extracting the functional features of the spectral curves. Finally, a novel spectral-spatial framework is designed for classification tasks of HSI data sets. Experimental results in three commonly used HSI data sets indicate that the proposed method is effective and leads to promising classification results compared with some benchmarking methods. More importantly, the work tries to diversify and develop the existing theory and methods of HSI classification from discrete (vector) data learning methods to continuous (functional) data learning methods.

Original languageEnglish
Article number8861296
Pages (from-to)841-851
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number2
Publication statusPublished - Feb 2020

Bibliographical note

Funding Information:
Manuscript received November 14, 2018; revised July 23, 2019; accepted August 27, 2019. Date of publication October 7, 2019; date of current version January 21, 2020. This work was supported in part by the Fundamental Research Funds for the Central Universities (WUT: 2017IVA071), in part by the National Natural Science Foundation of China under Grant 61502195, Grant 81671633, and Grant 61877021, and in part by the Natural Science Foundation of Hubei Province under Grant 2018CFB691. (Corresponding author: Yantao Wei.) Z. Ye and J. Chen are with the School of Science, Wuhan University of Technology, Wuhan 430070, China (e-mail:;

Publisher Copyright:
© 1980-2012 IEEE.

Other keywords

  • B-spline basis function
  • functional data discriminant analysis (FDDA)
  • hyperspectral image (HSI) classification
  • regularized weighted fitting model (RWFM)


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