Spectral Unmixing

Deciphering Earth's and Lunar materials with advanced machine learning.

Overview

This project delves into the fascinating world of spectral unmixing using machine learning. The goal is to decipher the composition of various Earth-based and Lunar materials by leveraging advanced deep learning models and feature engineering techniques. By analyzing the spectral signatures of these materials, the project seeks to provide valuable insights into Earth’s geological diversity, resource exploration, scientific discovery, and enhance our understanding of Lunar materials as well.

The project uses synthetic and real data from various Earth-based materials as proxies to lunar materials. These materials range from everyday products to synthetic spectral data, demonstrating the model’s adaptability and robustness.

Unveiling Earth’s and Lunar Geological Secrets

The researchers have harnessed the capabilities of machine learning to transform raw spectral data into informative features. These features capture the nuances of the materials, including periodic patterns, variations, and distinctive spectral characteristics. By doing so, they enhance the model’s ability to discern subtle differences in spectral signatures and make accurate predictions regarding the composition of mixed Earth-based and Lunar materials.

The Role of Feature Engineering

Feature engineering plays a pivotal role in the project’s methodology. Techniques such as Fourier coefficients, principal component analysis (PCA) components, and derivatives are employed to create valuable representations of the spectral data. This transformation of raw data into meaningful features is essential for the accurate analysis of Earth’s and Lunar geological materials.

Note: The methodology, results and findings in this file will be updated later after defending my thesis.

Key Features

  • Spectral unmixing for Earth-based and Lunar materials
  • Advanced feature engineering techniques
  • Adaptability to diverse datasets

Technologies

Machine Learning Frameworks: Pytorch, Sklearn, Scipy
Programming Languages: Python
Data Analysis Tools: Jupyter Notebook, NumPy, Pandas

Source Code:

Challenges & Future Directions

The project addresses various challenges associated with the analysis of Earth-based and Lunar materials, including the need for extensive and diverse datasets to mimic complex material mixtures. The model’s performance has been promising, but further improvements can be achieved with increased data samples, particularly in scenarios where base materials are similar.

In the future, the focus should be on acquiring more spectral data for Earth-based and Lunar materials, and developing more advanced machine learning models and feature engineering techniques to enhance the accuracy of composition predictions in challenging scenarios. This project represents a significant step toward unlocking Earth’s and Lunar geological secrets and advancing our understanding of the materials that make up our planet and the Moon.