Mapping Malaysian Urban Environment from Airborne Hyperspectral Sensor System in the VIS-NIR (0.4-1.1 µm) Spectrum

Abstract

Airborne hyperspectral remote sensing is a relatively new technology in Malaysia that needs to be tested for its feasibility. Various applications can benefit from the enormous potential offered such as in urban mapping in which rapid development in Malaysia can be accurately monitored. However, the use of hyperspectral data will also depend critically on the selection of suitable classifiers in order to extract the information. Hence, in this study, image classification was performed using various classifiers such as Parallelepiped, Minimum Distance, Mahalanobis Distance, Maximum Likelihood (ML), Spectral Information Divergence (SID), Spectral Angle Mapper (SAM), Binary Encoding (BE), Neural Network (NN) and Support Vector Machine (SVM). The accuracy of the classifiers was measured based on comparisons with ground truth data. SVM classifier shows the highest overall accuracy (87.98%) followed by ML with 83.17% and BE achieved the lowest accuracy with 39.28%. The findings indicate the feasibility of hyperspectral remote sensing for mapping urban environment in Malaysia with SVM as the most effective classifier for that purpose.