Neighboring Discriminant Component Analysis for Asteroid Spectrum Classification
| Autor: | Keping Yu, Xiao Ping Lu, Tan Guo, Yong Xiong Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
feature learning
Computer science business.industry deep space exploration dimension reduction Dimensionality reduction Science asteroid spectrum classification Pattern recognition classifier learning k-nearest neighbors algorithm Support vector machine extreme learning machine Deep space exploration Asteroid Feature (machine learning) General Earth and Planetary Sciences Artificial intelligence business Feature learning Extreme learning machine |
| Zdroj: | Remote Sensing; Volume 13; Issue 16; Pages: 3306 Remote Sensing, Vol 13, Iss 3306, p 3306 (2021) |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs13163306 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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