Accelerating an Imaging Spectroscopy Algorithm for Submerged Marine Environments Using Graphics Processing Units

Remote sensing is utilized across a wide array of disciplines, including resource management, disaster relief planning, environmental assessment, and climate change impact analysis. The data volume and processing requirements associated with remote sensing are rapidly expanding as a result of the increasing number of satellite and airborne sensors, greater data accessibility, and expanded utilization of data intensive technologies such as imaging spectroscopy. However, due to the limited ability of current computing systems to gracefully scale with application requirements, particularly in the desktop level market, large amounts of data are currently underutilized or never explored. Computing limitations thus constrain our ability to efficiently and accurately address key science questions using remote sensing. The current evolution in general purpose computing on Graphics Processing Units (GPUs), an emerging technology that is redefining the field of high performance computing, facilitates significantly improved computing capabilities for current and future image analysis needs. We demonstrate the advantages of this technology by accelerating an imaging spectroscopy algorithm for submerged marine habitats using GPU computing. Results indicate that considerable improvement in performance can be achieved using a single GPU on a standard desktop computer. This technology has enormous potential for continued growth exploiting high performance computing, and provides the foundation for significantly enhanced remote sensing capabilities.