Revisiting existing classification approaches for building materials based on hyperspectral data

Classification of materials found in urban areas using remote sensing, in particular with hyperspectral data, has in recent times increased in importance. This study is conducting classification of materials found on building using hyperspectral data, by using an existing spectral library and collected data acquired with a spectrometer. Two commonly used classification algorithms, Support Vector Machine and Random Forest, were used to classify the materials. In addition, dimensionality reduction and band selection were performed to determine if selected parts of the full spectral domain, such as the Short Wave Infra-Red domain, are sufficient to classify the different materials. We achieved the best classification results for the two datasets using dimensionality reduction based on a Principal Component Analysis in combination with a Random Forest classification. Classification using the full domain achieved the best results, followed by the Short Wave Infra-Red domain.