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Aggregation of Descriptive Regularization and Fuzzy Logic Techniques
for Enhanced Remote Sensing Imaging
A. Castillo Atoche, O. Palma Marrufo and R. Peon Escalante
Facultad de Ingenieria, Universidad Autonoma de Yucatan, M´erida, Mexico
Keywords:
Remote Sensing, Parallel Computing, GPUs.
Abstract:
In this paper, the aggregation of the descriptive regularization and Fuzzy-Logic techniques is proposed for the
enhancement/reconstruction of the power spatial spectrum pattern (SSP) of the wave field scattered from re-
motely sensed scenes. In particular, the Weighted Constrain Least Square (WCLS) and the Fuzzy anisotropic
diffusion techniques are algorithmically adapted and implemented in a parallel fashion using commodity
graphic processor units (GPUs) improving the time performance of real-time remote sensing applications.
Experimental results show the performance efficiency both in resolution enhancement and in computational
complexity reduction metrics with the presented approach.
1 INTRODUCTION
Advances in sensor technology are revolutionizing
the way of images are collected, managed and pro-
cessed. The incorporation of latest-generation sen-
sors to radar/SAR systems is currently producing
a near-continual stream of high-dimensional image
data. Such amount of collected information is now
required to be processed in (near) real-time mode for
newer applications in Earth monitoring, in medical
image fusion and enhancement, and computer vision.
Relevant examples include monitoring of natural dis-
asters like earthquakes and floods, military applica-
tions, tracking of man-induced hazards, forest fires,
oil spills and other types of biological agents. Also,
these applications need timely responses for swift
decision which depend upon real-time performance
of algorithm implementation (Henderson and Lewis,
1998),(Chang, 2007), (Goodman et al., 2011), (Yu
et al., 2013). Additionally, the computational com-
plexity of the advanced high-resolution remote sens-
ing (RS) and radar imaging techniques that employ
the recently developed regularization methods for en-
hanced radar imaging and remote sensing (RS) image
reconstruction/ enhancement procedures (Shkvarko
et al., 2008),(Castillo Atoche et al., 2010), (Shkvarko,
2010) is definitively unacceptable for a (near) real-
time implementation with any existing digital sig-
nal processor (DSP) or high-speed personal computer
(PC). In this regard, a tremendous amount of data pro-
cessing is required to be computed for different type
of image processing algorithms. To provide such high
computational demands under (near) real-time con-
straints, highly parallel processing schemes must be
developed. Usually, general-purposesystems are used
like multi-PC’s, field programmable gate arrays (FP-
GAs) or digital signal processing (DSP) platforms.
Therefore, the implementation of the aggregated de-
scriptive regularization and fuzzy anisotropic diffu-
sion techniques via GPU computing for real-time data
processing is considered in this study.
The principal innovation that distinguishes our ap-
proach from previous studies (Paz and Plaza, 2010),
(Castillo Atoche et al., 2009), (Liu and Plaza, 2011)
is twofold: first, the conceptualization and algorith-
mically aggregation of the weighted constrained least
square (WCLS) algorithm with the fuzzy Anisotropic
Diffusion technique for image enhancement is em-
ployed. In this stage fuzzy edge detectors are in-
troduced in order to provide a more flexible and ro-
bust way to define the edges instead of using the
well-known Laplacian filter as the edge factor in the
anisotropic diffusion. The essential idea is to avoid
blurring of the edges, after the WCLS reconstruc-
tion, with the incorporation of an edge stopping func-
tion which estimates the diffusion coefficients ensur-
ing the smoothing process only in the interior re-
gions without crossing the edges. Second, the al-
gorithmic implementation using massively processors
with a graphic processing unit (GPU) platform is per-
formed. Here, parallel computing techniques are used
in order to improve the time performance of the algo-
193
Castillo Atoche A., Palma Marrufo O. and Peon Escalante R..
Aggregation of Descriptive Regularization and Fuzzy Logic Techniques for Enhanced Remote Sensing Imaging.
DOI: 10.5220/0005154301930198
In Proceedings of the International Conference on Fuzzy Computation Theory and Applications (FCTA-2014), pages 193-198
ISBN: 978-989-758-053-6
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)