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Article

Neighboring Discriminant Component Analysis for Asteroid Spectrum Classification

by 1,2,3, 1,2,*, 1,2 and 4
1
Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau 999078, China
2
State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology, Taipa, Macau 999078, China
3
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
4
Global Information and Telecommunication Institute, Waseda University, Tokyo 169-8050, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Liangpei Zhan, Lefei Zhang, Qian Shi and Yanni Dong
Remote Sens. 2021, 13(16), 3306; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163306
Received: 1 July 2021 / Revised: 15 August 2021 / Accepted: 16 August 2021 / Published: 20 August 2021
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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). View Full-Text
Keywords: deep space exploration; asteroid spectrum classification; dimension reduction; feature learning; classifier learning; extreme learning machine deep space exploration; asteroid spectrum classification; dimension reduction; feature learning; classifier learning; extreme learning machine
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MDPI and ACS Style

Guo, T.; Lu, X.-P.; Zhang, Y.-X.; Yu, K. Neighboring Discriminant Component Analysis for Asteroid Spectrum Classification. Remote Sens. 2021, 13, 3306. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163306

AMA Style

Guo T, Lu X-P, Zhang Y-X, Yu K. Neighboring Discriminant Component Analysis for Asteroid Spectrum Classification. Remote Sensing. 2021; 13(16):3306. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163306

Chicago/Turabian Style

Guo, Tan, Xiao-Ping Lu, Yong-Xiong Zhang, and Keping Yu. 2021. "Neighboring Discriminant Component Analysis for Asteroid Spectrum Classification" Remote Sensing 13, no. 16: 3306. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163306

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