Parallel morphological processing of hyperspectral image data on heterogeneous networks of computers

Abstract

Recent advances in space and computer technologies are revolutionizing the way remotely sensed data is collected, managed and interpreted. The development of efficient techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. Although most currently available parallel processing strategies for hyperspectral image analysis assume homogeneity in the computing platform, heterogeneous networks of computers represent a very promising cost-effective solution expected to play a major role in the design of high-performance computing platforms for many on-going and planned remote sensing missions. This paper explores techniques for mapping morphological hyperspectral analysis algorithms, characterized by their scalability and sub-pixel accuracy, onto heterogeneous parallel computers. Important aspects in algorithm design are illustrated by using both homogeneous and heterogeneous parallel computing facilities available at NASA's Goddard Space Flight Center and University of Maryland. Experiments reveal that heterogeneous networks of workstations represent a source of computational power that is both accessible and applicable in many remote sensing studies.