Parallel Implementation of Hyperspectral Image Processing Algorithms

Abstract

Abstract-High computing performance of algorithm analysis is essential in many hyperspectral imaging applications, including automatic target recognition for homeland defense and security, risk/hazard prevention and monitoring, wild-land fire tracking and biological threat detection. Despite the growing interest in hyperspectral imaging research, only a few efforts devoted to designing and implementing well-conformed parallel processing solutions currently exist in the open literature. With the recent explosion in the amount and dimensionality of hyperspectral imagery, parallel processing is expected to become a requirement in most remote sensing missions. In this paper, we take a necessary first step towards the quantitative comparison of parallel techniques and strategies for analyzing hyperspectral data sets. Our focus is on three types of algorithms: automatic target recognition, spectral mixture analysis and data compression. Three types of high performance computing platforms are used for demonstration purposes, including commodity cluster-based systems, heterogeneous networks of distributed workstations and hardware-based computer architectures. Combined, these parts deliver a snapshot of the state of the art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of incorporating parallel computing models into hyperspectral remote sensing problems. I. INTRODUCTIONHyperspectral imaging has been transformed in less than 30 years from being a sparse research tool into a commodity product available to a broad user community [1]. The development of computationally efficient techniques able to transform massive volumes of hyperspectral data sets, collected on a daily basis, into scientific understanding is critical for space-based Earth science and planetary exploration. To address the computational needs introduced by hyperspectral imaging applications, a few efforts have been directed towards the incorporation of high-performance computing models and platforms in remote sensing missions. For instance, the Center of Excellence in Space and Data Information Sciences (CESDIS), located at NASA's Goddard Space Flight Center in Maryland, developed the concept of Beowulf cluster with the aim of creating a cost-effective parallel computing system from commodity components to satisfy specific computational requirements for the Earth and space sciences community [2]. Although most dedicated parallel machines for hyperspectral image information extraction and mining have been chiefly homogeneous in nature [3], a current trend in the design of systems for analysis and interpretation of high-dimensional data sets is to utilize highly heterogeneous distributed platforms [4],

Performance-Aware High-Performance Computing for Remote Sensing Big Data Analytics

“…While these components reduce the amount of payload, they can produce real/near-real-time analysis results at the same time as data is being obtained from the sensor. For this purpose, programmable hardware devices such as FPGAs can be used [41,42]. FPGAs are the digital integrated circuits which consist of an array of programmable logic blocks and reconfigurable interconnects that allow the blocks to be connected simply.…”