A metaheuristic feature-level fusion strategy in classification of urban area using hyperspectral imagery and LiDAR data

One of the most sophisticated recent data fusions in remote sensing has involved the use of LiDAR and hyperspectral data. Feature-level fusion strategy is applied based on extraction of several recent proposed spectral and structural features from hyperspectral and LiDAR data, respectively. In order to optimize classification performance, feature selection and determination of classifier parameters are carried out simultaneously. Referring to complexity of search space, cuckoo search as a powerful metaheuristic optimization algorithm is applied. Experiments show that the proposed method can improve the overall classification accuracy up to 6% with respect to only hyperspectral imagery. The obtained results show the classification improvement for the tree, residential and commercial classes is about 4%, 21% and 35%, respectively.