  With the emergence of passive and active optical sensors available for
geospatial imaging, information fusion across sensors is becoming ever more
important. An important aspect of single (or multiple) sensor geospatial image
analysis is feature extraction - the process of finding "optimal" lower
dimensional subspaces that adequately characterize class-specific information
for subsequent analysis tasks, such as classification, change and anomaly
detection etc. In recent work, we proposed and developed an angle-based
discriminant analysis approach that projected data onto subspaces with maximal
"angular" separability in the input (raw) feature space and Reproducible Kernel
Hilbert Space (RKHS). We also developed an angular locality preserving variant
of this algorithm. In this letter, we advance this work and make it suitable
for information fusion - we propose and validate a composite kernel local
angular discriminant analysis projection, that can operate on an ensemble of
feature sources (e.g. from different sources), and project the data onto a
unified space through composite kernels where the data are maximally separated
in an angular sense. We validate this method with the multi-sensor University
of Houston hyperspectral and LiDAR dataset, and demonstrate that the proposed
method significantly outperforms other composite kernel approaches to sensor
(information) fusion.
