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Feature extraction from acoustic and hyperspectral data by 2d local discriminant bases search

Kalkan, Habil
In this thesis, a feature extraction algorithm based on 2D Local Discriminant Bases (LDB) search is developed for acoustic and hyperspectral data. The developed algorithm extracts the relevant features by both eliminating the irrelevant ones and/or by merging the ones that do not provide extra information on their own. It is implemented on real world data to separate aflatoxin contaminated or high risk hazelnuts from the sound ones by using impact acoustic and hyperspectral data. Impact acoustics data is used to sort cracked and intact shell hazelnuts with high classifi cation accuracy. Hypespectral images of the shelled and roasted (SRT) hazelnuts are used for classi fication and the algorithm extracted the spectral and spatial-frequency features for that classifi cation. Aflatoxin concentration of the SRT category hazelnuts is automatically decreased to 0.7 ppb from 608 ppb by eliminating the detected contaminated ones.