Classification in hyperspectral images by independent component analysis, segmented cross-validation and uncertainty estimates
Beatriz Galindo-Prietoa and Frank Westadb aDepartment of Engineering Cybernetics (ITK), Norwegian University of Science and Technology (NTNU), Norway bDepartment of Engineering Cybernetics (ITK), Norwegian University of Science and Technology (NTNU), Norway and CAMO Software, Oslo, Norway. E-mail: [email protected]
Independent component analysis combined with various strategies for cross-validation, uncertainty estimates by jack-knifing and critical Hotelling’s T2 limits estimation, proposed in this paper, is used for classification purposes in hyperspectral images. To the best of our knowledge, the combined approach of methods used in this paper has not been previously applied to hyperspectral imaging analysis for interpretation and classification in the literature. The data analysis performed here aims to distinguish between four different types of plastics, some of them containing brominated flame retardants, from their near infrared hyperspectral images. The results showed that the method approach used here can be successfully used for unsupervised classification. A comparison of validation approaches, especially leave-one-out cross-validation and regions of interest scheme validation is also evaluated.