INVARIANT SUBMERGED MATERIAL RECOGNITION WITH FLUORESCENCE LIDAR AND SPARSITY-BASED APPROACHES

This work deals with submerged object recognition methodologies with fluorescence LIDAR that can be applied when no prior information about environmental conditions is available. Previous invariant methods rely upon conventional unconstrained and constrained subspace projection concepts. This paper investigates application of sparsity-based concepts within this framework. A method enforcing both L1 and L2 norm penalties is investigated. Both synthetic and real data are employed to evaluate the potential of the method. Experimental results reveal that sparsity-based methods can be useful in this context and deserve further investigation.