Neighboring discriminant component analysis for asteroid spectrum classification

Tan Guo, Xiao Ping Lu*, Yong Xiong Zhang, Keping Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


With the rapid development of aeronautic and deep space exploration technologies, a large number of high-resolution asteroid spectral data have been gathered, which can provide diagnostic information for identifying different categories of asteroids as well as their surface composition and mineralogical properties. However, owing to the noise of observation systems and the ever-changing external observation environments, the observed asteroid spectral data always contain noise and outliers exhibiting indivisible pattern characteristics, which will bring great challenges to the precise classification of asteroids. In order to alleviate the problem and to improve the separability and classification accuracy for different kinds of asteroids, this paper presents a novel Neighboring Discriminant Component Analysis (NDCA) model for asteroid spectrum feature learning. The key motivation is to transform the asteroid spectral data from the observation space into a feature subspace wherein the negative effects of outliers and noise will be minimized while the key category-related valuable knowledge in asteroid spectral data can be well explored. The effectiveness of the proposed NDCA model is verified on real-world asteroid reflectance spectra measured over the wavelength range from 0.45 to 2.45 µm, and promising classification performance has been achieved by the NDCA model in combination with different classifier models, such as the nearest neighbor (NN), support vector machine (SVM) and extreme learning machine (ELM).

Original languageEnglish
Article number3306
JournalRemote Sensing
Issue number16
Publication statusPublished - 2021 Aug 2


  • Asteroid spectrum classification
  • Classifier learning
  • Deep space exploration
  • Dimension reduction
  • Extreme learning machine
  • Feature learning

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)


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