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Chapter 2 High-Performance Computer Architectures for Remote Sensing Data Analysis: Overview and Case Study Antonio Plaza, University of Extremadura, Spain Chein-I Chang, University of Maryland, Baltimore Contents 2.1 Introduction ............................................................ 10 2.2 Related Work ........................................................... 13 2.2.1 Evolution of Cluster Computing in Remote Sensing ............... 14 2.2.2 Heterogeneous Computing in Remote Sensing .................... 15 2.2.3 Specialized Hardware for Onboard Data Processing ............... 16 2.3 Case Study: Pixel Purity Index (PPI) Algorithm .......................... 17 2.3.1 Algorithm Description ........................................... 17 2.3.2 Parallel Implementations ......................................... 20 2.3.2.1 Cluster-Based Implementation of the PPI Algorithm ..... 20 2.3.2.2 Heterogeneous Implementation of the PPI Algorithm .... 22 2.3.2.3 FPGA-Based Implementation of the PPI Algorithm ...... 23 2.4 Experimental Results ................................................... 27 2.4.1 High-Performance Computer Architectures ....................... 27 2.4.2 Hyperspectral Data .............................................. 29 2.4.3 Performance Evaluation .......................................... 31 2.4.4 Discussion ....................................................... 35 2.5 Conclusions and Future Research ........................................ 36 2.6 Acknowledgments ...................................................... 37 References ................................................................... 38 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 9 © 2008 by Taylor & Francis Group, LLC 10 High-Performance Computing in Remote Sensing 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 sensingdataprocessingalgorithm,isimplementedindifferenttypesofHPCplatforms such as a massively parallel multiprocessor, a heterogeneous network of distributed computers, and a specialized field programmable gate array (FPGA) hardware ar-chitecture. 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-of-the-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. 2.1 Introduction The development of computationally efficient techniques for transforming the mas-sive amount of remote sensing data into scientific understanding is critical for space-based earth science and planetary exploration [1]. The wealth of informa-tion provided by latest-generation remote sensing instruments has opened ground-breaking perspectives in many applications, including environmental modeling and assessment for Earth-based and atmospheric studies, risk/hazard prevention and re-sponse including wild land fire tracking, biological threat detection, monitoring of oil spills and other types of chemical contamination, target detection for military and defense/security purposes, urban planning and management studies, etc. [2]. Most of the above-mentioned applications require analysis algorithms able to provide a re-sponseinreal-ornear-real-time.Thisisquiteanambitiousgoalinmostcurrentremote sensingmissions,mainlybecausethepricepaidfortherichinformationavailablefrom latest-generation sensors is the enormous amounts of data that they generate [3, 4, 5]. A relevant example of a remote sensing application in which the use of HPC technologies such as parallel and distributed computing are highly desirable is hy-perspectral imaging [6], in which an image spectrometer collects hundreds or even thousands of measurements (at multiple wavelength channels) for the same area on the surface of the Earth (see Figure 2.1). The scenes provided by such sen-sors are often called “data cubes,” to denote the extremely high dimensionality of the data. For instance, the NASA Jet Propulsion Laboratory’s Airborne Visi-ble Infra-Red Imaging Spectrometer (AVIRIS) [7] is now able to record the vis-ible and near-infrared spectrum (wavelength region from 0.4 to 2.5 micrometers) of the reflected light of an area 2 to 12 kilometers wide and several kilometers long using 224 spectral bands (see Figure 3.8). The resulting cube is a stack of images in which each pixel (vector) has an associated spectral signature or ‘fin-gerprint’ that uniquely characterizes the underlying objects, and the resulting data volume typically comprises several GBs per flight. Although hyperspectral imaging © 2008 by Taylor & Francis Group, LLC Mixed pixel (soil + rocks) Pure pixel (water) 4000 3000 2000 1000 0 300 600 900 1200 1500 1800 2100 2400 Wavelength (nm) 4000 3000 2000 1000 0 300 600 900 1200 1500 1800 2100 2400 Mixed pixel (vegetation + soil) Wavelength (nm) 5000 4000 3000 2000 1000 0 300 600 900 1200 1500 1800 2100 2400 Wavelength (nm) Figure 2.1 The concept of hyperspectral imaging in remote sensing. © 2008 by Taylor & Francis Group, LLC 12 High-Performance Computing in Remote Sensing is a good example of the computational requirements introduced by remote sensing applications, there are many other remote sensing areas in which high-dimensional data sets are also produced (several of them are covered in detail in this book). How-ever, the extremely high computational requirements already introduced by hyper-spectralimagingapplications(andthefactthatthesesystemswillcontinueincreasing their spatial and spectral resolutions in the near future) make them an excellent case study to illustrate the need for HPC systems in remote sensing and will be used in this chapter for demonstration purposes. Specifically, the utilization of HPC systems in hyperspectral imaging applications has become more and more widespread in recent years. The idea developed by the computer science community of using COTS (commercial off-the-shelf) computer equipment, clustered together to work as a computational “team,” is a very attractive solution [8]. This strategy is often referred to as Beowulf-class cluster computing [9] and has already offered access to greatly increased computational power, but at a low cost(commensuratewithfallingcommercialPCcosts)inanumberofremotesensing applications [10, 11, 12, 13, 14, 15]. In theory, the combination of commercial forces driving down cost and positive hardware trends (e.g., CPU peak power doubling every 18–24 months, storage capacity doubling every 12–18 months, and networking bandwidthdoublingevery9–12months)offerssupercomputingperformancethatcan now be applied a much wider range of remote sensing problems. Although most parallel techniques and systems for image information processing employed by NASA and other institutions during the last decade have chiefly been homogeneous in nature (i.e., they are made up of identical processing units, thus sim-plifyingthedesignofparallelsolutionsadaptedtothosesystems),arecenttrendinthe design of HPC systems for data-intensive problems is to utilize highly heterogeneous computing resources [16]. This heterogeneity is seldom planned, arising mainly as a result of technology evolution over time and computer market sales and trends. In this regard, networks of heterogeneous COTS resources can realize a very high level of aggregate performance in remote sensing applications[17], and the pervasive availability of these resources has resulted in the current notion of grid computing [18], which endeavors to make such distributed computing platforms easy to utilize in different application domains, much like the World Wide Web has made it easy to distributeWebcontent.Itisexpectedthatgrid-basedHPCsystemswillsoonrepresent the tool of choice for the scientific community devoted to very high-dimensional data analysis in remote sensing and other fields. Finally, although remote sensing data processing algorithms generally map quite nicely to parallel systems made up of commodity CPUs, these systems are generally expensive and difficult to adapt to onboard remote sensing data processing scenarios, in which low-weight and low-power integrated components are essential to reduce mission payload and obtain analysis results in real time, i.e., at the same time as the data are collected by the sensor. In this regard, an exciting new development in the field of commodity computing is the emergence of programmable hardware devices such as field programmable gate arrays (FPGAs) [19, 20, 21] and graphic processing units (GPUs) [22], which can bridge the gap towards onboard and real-time analysis of remote sensing data. FPGAs are now fully reconfigurable, which allows one to © 2008 by Taylor & Francis Group, LLC High-Performance Computer Architectures for Remote Sensing 13 adaptively select a data processing algorithm (out of a pool of available ones) to be applied onboard the sensor from a control station on Earth. On the other hand, the emergence of GPUs (driven by the ever-growing demands of the video-game industry) has allowed these systems to evolve from expensive application-specific units into highly parallel and programmable commodity compo-nents. Current GPUs can deliver a peak performance in the order of 360 Gigaflops (Gflops), more than seven times the performance of the fastest ×86 dual-core proces-sor (around 50 Gflops). The ever-growing computational demands of remote sensing applications can fully benefit from compact hardware components and take advan-tage of the small size and relatively low cost of these units as compared to clusters or networks of computers. The main purpose of this chapter is to provide an overview of different HPC paradigms in the context of remote sensing applications. The chapter is organized as follows: r Section 2.2 describes relevant previous efforts in the field, such as the evo-lution of cluster computing in remote sensing applications, the emergence of distributed networks of computers as a cost-effective means to solve remote sensing problems, and the exploitation of specialized hardware architectures in remote sensing missions. r Section 2.3 provides an application case study: the well-known Pixel Purity Index (PPI) algorithm [23], which has been widely used to analyze hyper-spectral images and is available in commercial software. The algorithm is first briefly described and several issues encountered in its implementation are dis-cussed. Then, we provide HPC implementations of the algorithm, including a cluster-based parallel version, a variation of this version specifically tuned for heterogeneous computing environments, and an FPGA-based implementation. r Section 2.4 also provides an experimental comparison of the proposed imple-mentations of PPI using several high-performance computing architectures. Specifically, we use Thunderhead, a massively parallel Beowulf cluster at NASA’s Goddard Space Flight Center, a heterogeneous network of distributed workstations, and a Xilinx Virtex-II FPGA device. The considered application is based on the analysis of hyperspectral data collected by the AVIRIS instru- mentovertheWorldTradeCenterareainNewYorkCityrightaftertheterrorist attacks of September 11th. r Finally,Section2.5concludeswithsomeremarksandplausiblefutureresearch lines. 2.2 Related Work Thissectionfirstprovidesanoverviewoftheevolutionofclustercomputingarchitec-tures in the context of remote sensing applications, from the initial developments in Beowulf systems at NASA centers to the current systems being employed for remote © 2008 by Taylor & Francis Group, LLC ... - tailieumienphi.vn
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