Accelerating a hyperspectral inversion model for submerged marine ecosystems using high performance computing on graphical processor units

Remote sensing of shallow submerged marine ecosystems presents a challenging environment for information extraction algorithms, where physically based solutions commonly require complex, computationally intensive algorithms. The inherent variations in water depth, water properties, and surface waves all impact the measured remote sensing signal, and the strong absorption of light in water also limits the effective range of wavelengths available for analysis. An algorithm has been developed to address this multifaceted problem. The algorithm uses a two-stage inverse semi-analytical optimization model and spectral unmixing scheme to derive water column properties, water depth and habitat composition from imaging spectroscopy data. In addition to testing and validation studies, work on this algorithm has included improving its efficiency using the computing power of graphical processor units (GPUs). This improvement provides accelerated execution of the algorithm, and by leveraging more robust optimization routines, also facilitates increased accuracy in algorithm output. Initial results from implementing the algorithm on a single GPU using a conservative optimization strategy indicate substantial improvement in performance can be achieved using this technology. We present an overview of the algorithm, provide example output, discuss the GPU parallelization approach, and illustrate the performance achievements that have been obtained using GPU technology.