Hyperspectral classification of blood-like substances using machine learning methods combined with genetic algorithms in transductive and inductive scenarios

• URL: http://arxiv.org/abs/2011.02188v1
• Date: Wed, 4 Nov 2020 09:18:16 GMT
• Title: Hyperspectral classification of blood-like substances using machine learning methods combined with genetic algorithms in transductive and inductive scenarios
• Authors: Filip Pa{\l}ka, Wojciech Ksi\k{a}\.zek, Pawe{\l} P{\l}awiak, Micha{\l} Romaszewski, Kamil Ksi\k{a}\.zek
• Abstract summary: This study is focused on applying genetic algorithms (GA) to model and band selection in hyperspectral image classification. We use a forensic-inspired data set of seven hyperspectral images with blood and five visually similar substances to test GA-optimised classifiers.
• Score: 3.7361116450006877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study is focused on applying genetic algorithms (GA) to model and band selection in hyperspectral image classification. We use a forensic-inspired data set of seven hyperspectral images with blood and five visually similar substances to test GA-optimised classifiers in two scenarios: when the training and test data come from the same image and when they come from different images, which is a more challenging task due to significant spectra differences. In our experiments we compare GA with a classic model optimisation through grid search. Our results show that GA-based model optimisation can reduce the number of bands and create an accurate classifier that outperforms the GS-based reference models, provided that during model optimisation it has access to examples similar to test data. We illustrate this with experiment highlighting the importance of a validation set.

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