Image Processing Reference
In-Depth Information
7
High-Speed VLSI Architecture Based on
Massively Parallel Processor Arrays for
Real-Time Remote Sensing Applications
A. Castillo Atoche 1 , J. Estrada Lopez 2 ,
P. Perez Muñoz 1 and S. Soto Aguilar 2
1 Mechatronic Department, Engineering School, Autonomous University of Yucatan
2 Computer Engineering Dept., Mathematics School, Autonomous University of Yucatan
Mexico
1. Introduction
Developing computationally efficient processing techniques for massive volumes of
hyperspectral data is critical for space-based Earth science and planetary exploration (see for
example, (Plaza & Chang, 2008), (Henderson & Lewis, 1998) and the references therein).
With the availability of remotely sensed data from different sensors of various platforms
with a wide range of spatiotemporal, radiometric and spectral resolutions has made remote
sensing as, perhaps, the best source of data for large scale applications and study.
Applications of Remote Sensing (RS) in hydrological modelling, watershed mapping, energy
and water flux estimation, fractional vegetation cover, impervious surface area mapping,
urban modelling and drought predictions based on soil water index derived from remotely-
sensed data have been reported (Melesse et al., 2007). Also, many RS imaging applications
require a response in (near) real time in areas such as target detection for military and
homeland defence/security purposes, and risk prevention and response. Hyperspectral
imaging is a new technique in remote sensing that generates images with hundreds of
spectral bands, at different wavelength channels, for the same area on the surface of the
Earth. Although in recent years several efforts have been directed toward the incorporation
of parallel and distributed computing in hyperspectral image analysis, there are no
standardized architectures or Very Large Scale Integration (VLSI) circuits for this purpose in
remote sensing applications.
Additionally, although the existing theory offers a manifold of statistical and descriptive
regularization techniques for image enhancement/reconstruction, in many RS application
areas there also remain some unsolved crucial theoretical and processing problems related
to the computational cost due to the recently developed complex techniques (Melesse et al.,
2007), (Shkvarko, 2010), (Yang et al., 2001). These descriptive-regularization techniques are
associated with the unknown statistics of random perturbations of the signals in turbulent
medium, imperfect array calibration, finite dimensionality of measurements, multiplicative
signal-dependent speckle noise, uncontrolled antenna vibrations and random carrier
trajectory deviations in the case of Synthetic Aperture Radar (SAR) systems (Henderson &
Lewis, 1998), (Barrett & Myers, 2004). Furthermore, these techniques are not suitable for
Search WWH ::




Custom Search