Comparative analysis of hyperspectral feature extraction methods in vegetation classification
To perform an accurate vegetation classification in hyperspectral data, feature extraction process prior to classification is very important. Success rates of classifiers in vegetation are rather limited compared to classification of other types of materials. Therefore, it is required to perform an effective feature extraction before classification. Principle Component Analysis(PCA) is a common and easily applicable method for this purpose. However, PCA is not an optimal method for distinguishing between different plant species. In this study, the reasons for PCA not being an adequate method for this purpose are discussed and alternative useful feature extraction methods in classification of plant species are examined. Tests were performed for Spectrally Segmented PCA(SSPCA), Discrete Wavelet Transform(DWT) and Genetic Algorithm(GA) feature extraction methods, their effects on classifier performances were compared and it was observed that all of the mentioned alternatives had noticable improvements in classification performances.