Recent developments and future directions in parallel processing of remotely sensed hyperspectral images

Remotely sensed hyperspectral imaging is a technique that generates hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. Computationally effective processing of these image cubes can be greatly beneficial in many application domains, including environmental modeling, risk/hazard prevention and response, or defense/security. With the aim of providing an overview of recent developments and new trends in the design of parallel and distributed systems for hyperspectral image analysis, this paper discusses and inter-compares four different strategies for efficiently implementing a standard hyperspectral image processing chain: 1) commodity Beowulf-type clusters, 2) heterogeneous networks of workstations, 3) field programmable gate arrays (FPGAs), and 4) graphics processing units (GPUs). Combined, these parts deliver a snapshot of the state-of-the-art in those areas, and a thoughtful perspective on the potential and emerging challenges of adapting high performance computing systems to remote sensing problems.