Classification of Urban Materials Using Artificial Color Features for Hyperspectral Data

Selection of appropriate features is important for classification of urban materials using hyperspectral data. Urban materials lack dominant diagnostic absorption and hence features representing complete spectrum are likely to provide better classification performance. Furthermore, selection of appropriate features for a given data requires empirical assessment. In the present work, we introduce artificial color features that take into account complete spectrum. In addition to the color features, we use reflectance values of all the noise free wavelengths of EO-l Hyperion (set A), and a wavelength set H={445,576,638,759,1100, 1316, 1989} reported in literature. We classify EO-l Hyperion image of Pune city using multiple classifiers and compare their outcome. The color values, set A, and H provide similar results. Set H and color features results in minor drop of accuracies in urban classes such as industrial roofs and residential concrete roofs.