IEEE Geoscience and Remote Sensing Magazine - June 2013 - 29

approaches in the literature and
introduced the principles and
standard terminology. The use
and performance of the different existing approaches were
illustrated in a real problem of
vegetation monitoring which
confirmed the excellent results
obtained by nonparametric sta(a)
(b)
(c)
tistical approaches. The field
encompasses both physics of
FIGURE 15. Chlorophyll concentration estimated map (a), predictive standard deviation (b), and
land and atmosphere, optimimasked confidence map (c) generated with GP on the CHRIS 12-07-2003 nadir image.
zation, and machine learning.
The future poses some chalsets. Specifically, we describe techniques based on different
lenging problems: the community will be confronted to the
types of hardware accelerators (see Fig. 16), such as clusters,
availability of huge amount of training data coming from
distributed platforms and specialized devices such as comRTMs, the design of more sophisticated and realistic RTMs,
modity graphics processing units (GPUs) or field programthe combination of both statistical and physically-based
mable gate arrays (FPGAs). The section concludes with a
models, and the specification of models that can adapt to
summary of the main challenges in the exploitation of HPC
multitemporal domains.
platforms in hyperspectral remote sensing missions.
vII. Fast compUtInG
A. clusteRs AnD DistRiButeD
In this section, we outline several strategies to accelerate
plAtFoRMs FoR hypeRspectRAl pRocessing
hyperspectral image computations using different kinds of
Clusters were originally developed with the purpose of creathigh-performance computing (HPC) architectures. As mening a cost-effective parallel computing system able to satisfy
tioned before in this contribution, the improved spatial,
specific computational requirements in different applicaspectral and temporal resolutions provided by hyperspections. In remote sensing, the need for large amounts of comtral instruments demand fast computing solutions that can
putation was first identified for processing multispectral
accelerate the efficient exploitation of hyperspectral data

Advanced Earth
Observation
Capabilities

Increased Spatial,
Spectral and
Temporal Resolution

Spectral Signature Development
Hyperspectral
Pixel

Hyperspectral Image

Spectral Signature
Reflectance

The Spectral Signature of a Pixel
Is a Combination of the Reflected
or Emitted Energy from All the
Features That Fall Within That
Pixel Area.

Real-Time
Hardware
Requirements Accelerators

0.4
2.5
Wavelength (µm)

FIGURE 16. Different types of hardware accelerators commonly used to improve computational performance of hyperspectral

imaging applications.
june 2013

ieee Geoscience and remote sensinG maGazine

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