Table Of ContentAdvanced Geoscience Remote Sensing
Edited by Maged Marghany
Advanced Geoscience Remote Sensing
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Copyright © 2014
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Published in print & online 5 June 2014
ISBN-10 9535115812
ISBN-13 978-9535115816
C ontents
Preface
Chapter 1 Mathematical Description of Bayesian Algorithm for
Speckle Reduction in Synthetic Aperture Radar Data
by Daniela Coltuc
Chapter 2 Oil-Spill Pollution Remote Sensing by Synthetic
Aperture Radar
by Yuanzhi Zhang, Yu Li and Hui Lin
Chapter 3 Simulation of Tsunami Impact on Sea Surface Salinity
along Banda Aceh Coastal Waters, Indonesia
by Maged M arghany
Chapter 4 HF Radar Network Design for Remote Sensing of the
South China Sea
by S. J. Anderson
Chapter 5 A Three -dimensional of Coastline Deformation using the
Sorting Reliability Algorithm of ENVISAT Interferometric
Synthetic Aperture Radar
by Maged Marghany
Chapter 6 On the ??dification of Nu clear Chronometry in Astrophysics
and in Geophysics
by V.S. Olkhovsky
Chapter 7 Exploring and Using the Magnetic Methods
by Othniel K. Likkason
Chapter 8 Identific ation of Communication Channels for Remote Sensing
Systems Using Volterra Model in Frequency Domain
by Vitaliy Pavlenko and Viktor Speranskyy
Chapter 9 Statistical Characterization of Bare Soil Surface Microrelief
by Edwige Vannier, Odile Taconet, Richard Dussÿaux and
Olivier Chimi-Chiadjeu
VI Contents
Chapter 10 Oil Spill Pollution Automatic Detection from MultiSAR Satellite
Data Using Genetic Algorithm
by Maged Marghany
Chapter 11 Morphodynamic Environment in a Semiarid Mouth River Complex
Choapa River , Chile
by Joselyn Arriagada, Marÿa-Victoria Soto and Pablo Sarricolea
Preface
Nowadays advanced remote sensing technology plays tremendous roles
in building a quantitative and comprehensive understanding of how
the Earth system works.
The advanced remote sensing technology is also used widely to monitor
and survey the natural disasters and man-made pollution.
Besides, telecommunication is considered as an accuracy remote sensing
advanced technology tool.
Precise usage of remote sensing and telecommunication cannot be built
without comprehensive understanding of mathematics and physics.
This book has three parts
(i) microwave remote sensing applications,
(ii) nuclear, geophysics and telecommunication; and
(iii) environment remote sensing investigations.
Chapter 1
Mathematical Description of Bayesian Algorithm for
Speckle Reduction in Synthetic Aperture Radar Data
Daniela Colţuc
Additional information is available at the end of the chapter
http://dx.doi.org/10.5772/57529
1. Introduction
In remote sensing applications, the Synthetic Aperture Radar (SAR) images constitute a
distinct class. The main particularity of this class is the image radiometry, which is a product
between two signals: the terrain radar reflectivity R and the speckle s. The reflectivity depends
on the terrain nature while the speckle is the consequence of the surface roughness. The speckle
phenomenon is intrinsic to the radar technique. The radar sensor sends a coherent fascicle of
microwaves toward the ground, which is scattered at the contact with the surface. The SAR
antenna receives a non-coherent radiation resulted by the superposition of the reflected
microwaves. These microwaves have random phase because of the surface roughness. The
result is an image with a noisy aspect even in the regions with constant radar reflectivity. An
example is the image in Figure 1, which represents a town surrounded by cultivated fields in
French Guyana.
Although the speckle is carrying information that can be used in a series of applications, there
are many cases where only R is of interest. In these situations, the speckle must be reduced in
order to fully exploit the information in R.
The methods used to extract the reflectivity R from the SAR signal are known under the name
of scene reconstruction filters. Among them, some of the most accurate in reconstruction are the
Bayesian filters. These filters are based on the estimation theory, which has found a fertile
terrain in SAR imagery due to the existing models for speckle. In the next section, we give a
short overview of speckle distribution models in SAR imagery.
The Bayesian filters for speckle reduction were developed first for single SAR images and then
extended to the multi-channel case. Here, the Bayesian filters have gained in efficiency due to
the enhanced statistical diversity of the 3D data. This chapter presents several solutions for the
