Neural network classification of hyperspectral imagery for urban environments: A case study

Urban environments are complex because many different artificial and natural objects occur in close proximity. Being able to understand the processes and workings of these environments requires the ability to observe and record data with high spatial and spectral resolution. Hyperspectral sensors have been gaining popularity for this task as they are becoming more affordable. In this research, a commonly used maximum likelihood (ML) classifier and artificial neural network (ANN) classifier have been compared for classifying urban land use and land cover (LULC) using AISA+ hyperspectral data. Further, the best set of bands were identified for classification of urban areas for use in ANN classification. Optimum bands based on a spectral separability measure were used with a neural network classifier to compare its performance with maximum likelihood classifier. It was found that both the classifiers had an overall classification accuracy of more than 80% and the neural network classifier with optimum band selection performed better in all of the study sites.