2022
AAAI
AAAI 2022
Unsupervised Identification of Materials with Hyperspectral Images
Abstract
Abstract We introduce a novel technique to identify three spectra representing the three primary materials in a hyperspectral image of a scene. We accomplish this using a modified autoencoder. Further research will be conducted to verify the accuracy of these spectra.
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Interdisciplinary Bridge
— Computer Vision and Deep Learning and Machine Learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Unsupervised Learning
Deep Learning > Architectures > Autoencoders
Computer Vision > Processing > Image Processing
Deep Learning > Learning Types > Unsupervised Learning
Deep Learning > Models > Autoencoders