Sequential Classification of Hyperspectral Images

Abstract

For sugar producers, it is a major problem to detect contamination of sugar. Doing it manually would not be feasible because of the high demand and would require toomuch labor. This report evaluates if the problem can be solved by using a hyperspectral camera operating in a wavelength range of 400-1000 nm and a spectralresolution of 224. Using the machine learning algorithms Artificial Neural Networkand Support Vector Machine, models were trained on pixels labeled as sugar or different materials of contamination. An autonomous system could be developed to analyze the sugar in real time and remove the contaminated sugar. This paper presents the results from using both Artificial Neural Networks as well as SupportVector Machine. It also addresses the impact of the pre-processing techniques filtering and maximum normalization when applying machine learning algorithms. The results showed that the accuracy can be significantly increased by using a hyperspectral camera instead of a normal camera, especially for plastic materials where using anormal camera gave a precision and recall score of 0.0 while using the hyperspectral camera gave above 0.9. Support Vector Machine performed slightly better than using Artificial Neural Network, especially for plastic material. The filtering and themaximum normalization did not increase the accuracy and could therefore be omitted in favor for performance.