- Hyperspectral Image Classification of Liquids: The HyL12 Dataset
- Project Year:
2017
- REU Student (s):
Andrea Burns | Tulane University
- Student 1 Institution:
Tulane University
- Project Mentor:
Waheed Bajwa
- Project Mentor Area:
Electrical and Computer Engineering
- Project Abstract:
As demand rises for machine learning tasks such as classification and regression in many fields, state-of-the-art algorithms and new data collection techniques are necessary to
continue to improve performance. Tools such as hyperspectral
imaging have been introduced to increase available information,
and therefore increase prediction accuracy as well. This creates
motivation to curate more hyperspectral datasets, as improved
image classification can be achieved through introducing additional
spectral bands. Here we introduce a newly curated
Hyperspectral Liquid 12-Band dataset (HyL12) that consists
twelve classes including eleven liquids and an additional empty
class. This dataset can be expanded for security uses to help
liquid classification in contexts of safety concerns such as airport
security, concerts, and politic events.
