IEEE Geoscience and Remote Sensing Magazine - June 2013 - 30
imagery with tens of bands. As sensor instruments incorporated hyperspectral capabilities, it was soon recognized
that computer mainframes and mini-computers could not
provide sufficient power for effectively processing this kind
of data. It is worth noting that NASA and ESA are currently
supporting massively parallel clusters for remote sensing applications including hyperspectral imaging, such as
the Columbia supercomputer16 at NASA Ames Research
Center. Another example of massively parallel computing
facility which has been exploited for hyperspectral imaging
applications is located at the High Performance Computing Collaboratory (HPC2) at Mississippi State University17,
which has several supercomputing facilities that have been
used in hyperspectral imaging studies.
Homogeneous clusters have already offered access to
greatly increased computational power at a low cost (commensurate with falling commercial PC costs) in a number
of hyperspectral imaging applications, such as classification or spectral unmixing [15]. However, a recent trend in
the design of HPC systems for data-intensive problems,
such as those involved in hyperspectral image analysis, is
to utilize highly heterogeneous computing resources [176].
In this regard, networks of heterogeneous workstations can
realize a very high level of aggregate performance in hyperspectral imaging applications, and the pervasive availability of these resources resulted in the current notions of
grid and, later, cloud computing, which are yet to be fully
exploited in hyperspectral imaging problems [177].
Although hyperspectral processing algorithms generally
map quite nicely to clusters or networks of 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 is
collected by the sensor. In this regard, the emergence of
specialized hardware devices such as FPGAs [178] or
GPUs [179] exhibit the potential to bridge the gap towards
onboard and real-time analysis of remote sensing data.
B. gpus FoR hypeRspectRAl pRocessing
In recent years GPUs have evolved into highly parallel, multithreaded, many-core coprocessors with tremendous computational power, consumption and memory bandwidth [179].
The combined features of general-purpose supercomputing,
high parallelism, high memory bandwidth, low cost, compact size, and excellent programmability are now making
GPU-based desktop computers an appealing alternative to
a massively parallel systems made up of commodity CPUs.
The exploding GPU capability has attracted more and more
scientists and engineers to use it as a cost-effective highperformance computing platform, including scientists in
hyperspectral processing areas. In addition, GPUs can also
significantly increase the computational power of clusterbased and distributed systems (e.g., the fastest supercomputers in the world are now clusters of GPUs18).
Several efforts exploiting GPU technology can already
be found in the hyperspectral imaging literature [15], [16],
[180]. For instance, only in the area of spectral unmixing
there have been many developments already. A GPU-based
implementation of an automated morphological endmember extraction (AMEE) algorithm for pure spectral
signature (endmember) identification is described in [181].
In this case, speedups on the order of 15# were reported.
A full spectral unmixing chain comprising the automatic
estimation of the number of endmembers, the identification of the endmember signatures, and quantification of
endmember fractional abundances has been reported in
[182] with speedups superior to 50#. Additional efforts
towards real-time and on-board hyperspectral target detection and classification [183], [184] using GPUs have also
been recently available. It should be noted that, despite
the increasing programmability of low-power GPUs such
as those available in smartphones, radiation-tolerance and
power consumption issues still prevent the full incorporation of GPUs to spaceborne Earth observation missions.
c. FpgAs FoR hypeRspectRAl pRocessing
An FPGA [178] can be roughly defined as an array of interconnected logic blocks. One of the main advantages of
these devices is that both the logic blocks and their interconnections can be (re)configured by their users as many
times as needed in order to implement different combinational or sequential logic functions. This characteristic
provides FPGAs with the advantages of both software and
hardware systems in the sense that FPGAs exhibit more
flexibility and shorter development times than application
specific integrated circuits (ASICs) but, at the same time,
are able to provide much more competent levels of performance, closer to those offered by GPUs (but with much
lower power consumption). In fact, the power and energy
efficiency of FPGAs has significantly improved during the
last decade. FPGA vendors have achieved this goal improving the FPGA architectures, including optimized hardware
modules, and taking advantage of the most recent silicon
technology. For instance, manufacturing companies such
as Xilinx19 or Altera20 have reported a 50% reduction in
the power consumption when moving from their previous
generation of FPGAs. This feature, together with the availability of more FPGAs with increased tolerance to ionizing
radiation in space, have consolidated FPGAs as the current
standard choice for on-board hyperspectral remote sensing.
In the following, we outline several hyperspectral analysis
techniques that have been recently implemented in FPGAs.
If we consider the area of spectral unmixing, implementation of endmember extraction algorithms using a Xilinx
18
16
http://www.nas.nasa.gov/Resources/Systems/columbia.html
17
http://www.hpc.msstate.edu
30
http://www.top500.org
http://www.xilinx.com
20
http://www.altera.com
19
ieee Geoscience and remote sensinG maGazine
june 2013
http://www.xilinx.com
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