Spectral deconvolution using machine learning for determining material compositions in mixed samples
Date of Award
2024
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Committee Chair
Tathagata Mukherjee
Committee Member
Manil Maskey
Committee Member
Chaity Banerjee Mukherjee
Research Advisor
Tathagata Mukherjee
Subject(s)
Materials--Analysis, Machine learning, Hyperspectral imaging
Abstract
Hyperspectral unmixing is essential in determining material compositions in mixed samples with a larger number of endmembers, but achieving precise decom- position remains challenging due to the limited work done, the presence of spectral variability, and endmember complexities. This study proposes many machine learning methods and techniques to identify the material composition in mixed samples (composites). Other methods to solve this problem mainly focus on a very small number of endmembers; our approach works with many end members, improving the model’s robustness. The proposed methods effectively tackle spectral variability, high dimensionality, and the presence of similar endmembers by integrating a multilayer perceptron model with Fast Fourier Transform feature extraction of the derivatives features, an original sampling technique, and a weighted loss function. The findings illustrate the robustness of the model in different datasets with the importance of using synthetic and real data for training the underlying models. The results show that the proposed methods improve the performance, with applications to material characterization and land cover mapping.
Recommended Citation
Chaudhary, Prakash, "Spectral deconvolution using machine learning for determining material compositions in mixed samples" (2024). Theses. 676.
https://louis.uah.edu/uah-theses/676