Neural network for classification of asteroid spectraDavid Korda1 , Antti Penttilä2 , Arto Klami3 , and Tomáš Kohout1,4
1

Department of Geosciences and Geography, University of Helsinki, Finland2
Department of Physics, University of Helsinki, Finland3
Department of Computer Science, University of Helsinki, Finland
4
Institute of Geology, Czech Academy of Sciences, Czech Republic

Introduction
Asteroid mineral composition is important parameter in planetary science, planetary
defence, and in-space resource utilisation. Currently-used methods provide us mainly
quantitative information about the asteroid composition. The methods are based onempirical relations between spectral parameters (band areas, positions, depths), or on
spectral unmixing, and are highly sensitive to quality and consistency of input reflectancespectra. We test artificial neural networks as a tool which can infer mineral composition
directly from the reflectance values.

Methods
Artificial neural networks are used in tasks which are very difficult to define with exact
mathematics. A sensitivity of the networks to quality of input data (e.g., absolutereflectance values or variations in spectral slope) can be partially suppressed. The
networks are made of layers of neurons. Each neuron in a layer is non-linearly connectedwith neurons in the previous and following layers. The non-linearity and complexity ofneural network enable them to solve various tasks.
We use neural network for computing of mineral composition of the most common
silicates presented in meteorites (olivine, orthopyroxene, clinopyroxene, and plagioclase).
Our neural network composed of the input layer with reflectance values, one ‘intermediate’layer (hidden layer), and the output layer. The outputs are modal composition of theminerals and their chemical composition represented with end-members.

Data
We utilised measured reflectance spectra from the Relab database1 . We are motivated by
the ASPECT instrument (part of Hera/Milany CubSat) which will carry-out observations
in visible and near-infrared part of spectra. For this reason, we chose spectra which coverinterval between 350 nm and 2550 nm with the resolution 15 nm or better.

Results
Olivine and orthopyroxene are the most common minerals in ordinary chondrites. Therefore, we firstly applied the neural network on a set of olivine and pyroxene spectra.
1

http://www.planetary.brown.edu/relabdata/1Figure 1: Results of chemical composition. Top row: olivine. Bottom row: pyroxene.
The neural network contained only one hidden layer with 30 neurons. The outputs
were volume fractions of the minerals and their chemical composition. We trained thenetwork on about 80% of the whole dataset and used the remaining 20% to test theaccuracy of the predictions. The results obtained from the 20% are shown in Fig. 1.
The vertical axes show the correct (previously published) values while the horizontal
axes the values predicted by the neural network. In the top row, there are iron andmagnesium composition in olivine. In the bottom row, there are iron, magnesium, andcalcium composition in pyroxene.

Discussion
Except a few outlier, the predicted composition is usually within about 10% from theactual composition. The outliers might be a consequence of a limited training dataset orambiguously determined actual composition. In the near future, the predictive capabilityof the network will be improved via optimisation of its architecture and due to increase
number of training samples. In the next step, we will train the network on syntheticspectra of olivine, orthopyroxene, and plagioclase and evaluate the network on real
ordinary chondrites.

2