A Novel Composite Kernel Approach for Multisensor Remote Sensing Data Fusion

The increased availability of active and passive data captured over the same scene of interest makes it desirable to jointly utilize multisensor data to perform accurate classification. This paper proposes a novel fusion approach to integrate hyperspectral and LiDAR-derived digital surface model for land-cover classification. In this context, we propose a novel multisensor composite kernel technique based on extreme learning machines (named as multisensor composite kernels (MCKs)), which is capable of combining different methods in the feature fusion level in an effective way. In the proposed approach, we use extinction profiles to extract spatial and elevation features of hyperspectral and LiDAR data. Then, hyperspectral Stein's unbiased risk estimator (HySURE) is applied to identify the subspace (informative features) of spectral, spatial, and elevation features. Finally, MCK is applied to the extracted spectral, spatial, and elevation features to produce the final classification map. Results obtained by the proposed approach reveal the fact that this approach can effectively fuse and classify hyperspectral and LiDAR images and improve the classification accuracy of each data source significantly. In addition, the proposed method is fully automatic.