Comparison of supervised classification algorithms using a hyperspectral image for land use land cover classification

Hyperspectral Imaging is getting popular in land use land classification because of its ability to capture detailed information through higher spatial resolution and contagious spectral bands. Using the hyperspectral image from G-LiHT (Goddard’s LiDAR, Hyperspectral, and Thermal) Airborne Imager covering a study area in Tennessee, Knoxville, we compared the performance of Spectral Angle Mappers (SAM), Spectral Information Divergence (SID), and Support Vector Machine (SVM) for land use land cover classification. We used a confusion matrix for the accuracy assessment of the classifiers. Among the three classifiers, SVM showed the highest accuracy with 92.03%. Our results also show that some classes, such as water and forests, are consistently distinguishable across all classification methods, while others, such as built-up areas vary depending on the technique used.