ABSTRACT
Advances in sensor technology are revolutionizing the way remotely sensed data are collected, managed, and analyzed. In particular, many current and future applications of remote sensing in earth science, space science, and soon in exploration science require real-or near-real-time processing capabilities. In recent years, several efforts
have been directed towards the incorporation of high-performance computing (HPC) models to remote sensing missions. In this chapter, an overview of recent efforts in the design of HPC systems for remote sensing is provided. The chapter also includes an application case study in which the pixel purity index (PPI), a well-known remote sensing data processing algorithm, is implemented in different types of HPC platforms such as a massively parallel multiprocessor, a heterogeneous network of distributed computers, and a specialized field programmable gate array (FPGA) hardware architecture. Analytical and experimental results are presented in the context of a real application, using hyperspectral data collected by NASA’s Jet Propulsion Laboratory over the World Trade Center area in New York City, right after the terrorist attacks of September 11th. Combined, these parts deliver an excellent snapshot of the state-ofthe-art of HPC in remote sensing, and offer a thoughtful perspective of the potential and emerging challenges of adapting HPC paradigms to remote sensing problems.
