Application of an object-oriented feature extraction method to quantitative estimation from hyper-spectral data

Extracting significant features is essential for processing, storing and/or transmission of a vast volume of hyperspectral data. Conventional ways of extracting features are not always satisfactory for this kind of data in terms of optimality and computation time. The authors have already developed an object-oriented feature extraction method designed for supervised classification. They apply the basic idea of the approach to feature extraction for quantitative estimation from hyperspectral data. After the data obtained for various values of a quantity are orthogonalized and reduced by principal component analysis, the features describing the variation of spectra are extracted as linear combinations of the reduced components. An experiment using pigment shows that the feature extraction method for quantitative analysis yielded satisfactory results.