Multisensor Composite Kernels Based on Extreme Learning Machines
Pedram Ghamisi, Behnood Rasti, Jón Atli Benediktsson · IEEE Geoscience and Remote Sensing Letters · 2018
In this letter, we first propose multisensor composite kernel (MCK) extreme learning machines to fuse hyperspectral and light detection and ranging (LiDAR) features effectively. Then, based on the MCK, we develop a fully automatic fusion framework. In the proposed framework, spatial and elevation features of hyperspectral and LiDAR data are first extracted using extinction profiles. Then, hyperspectral Stein's unbiased risk estimator is utilized to extract the subspace (informative features) of spectral, spatial, and elevation features. The obtained results indicate that the proposed approach can successfully integrate and classify hyperspectral and LiDAR images to provide accurate classification results classification accuracies in an automatic manner.