Principles of Synthetic Aperture Radar Imaging: A System Simulation Approach - CRC Press Book
Author(s) Bio
Kun-Shan Chen
received a PhD degree in electrical engineering from the University of
Texas at Arlington in 1990. From 1992 to 2014, he was with the faculty
of National Central University, Taiwan. He joined the Institute of
Remote Sensing and Digital Earth, Chinese Academy of Science, in 2014,
and has served the Department of Electrical Engineering, The University
of Texas at Arlington, USA, as a research professor since 2014. He has
authored or coauthored over 120 journal papers, contributed seven book
chapters, is a coauthor of one book, and a fellow of The Institute of
Electrical and Electronics Engineers (IEEE).
December 18, 2015
by CRC Press
Reference
- 203 Pages
- 29 Color & 123 B/W Illustrations
ISBN 9781466593145 - CAT# K20597
Series:
Signal and Image Processing of Earth Observations
CRCnetBASE - SAR Models
UC San Diego /All Collec
Features
- Includes numerical analysis of system parameters, including platforms, sensor, and image focusing, and their influences
- Brings a large volume of samples of simulation on various scenarios to help readers resolve their own problems of interest
- Explains in details the state-of-the-art of space-, air-borne, and
ground-based systems, their different technical aspects and challenges
to overcome
- Presents novel processing algorithms and applications to feature extraction, target classification, and change detection
Summary
Principles of Synthetic Aperture Radar Imaging: A System Simulation Approach
demonstrates the use of image simulation for SAR. It covers the various
applications of SAR (including feature extraction, target
classification, and change detection), provides a complete understanding
of SAR principles, and illustrates the complete chain of a SAR
operation.
The book places special emphasis on a ground-based SAR, but also
explains space and air-borne systems. It contains chapters on signal
speckle, radar-signal models, sensor-trajectory models, SAR-image
focusing, platform-motion compensation, and microwave-scattering from
random media.
While discussing SAR image focusing and motion compensation, it
presents processing algorithms and applications that feature extraction,
target classification, and change detection. It also provides samples
of simulation on various scenarios, and includes simulation flowcharts
and results that are detailed throughout the book.
Introducing SAR imaging from a systems point of view, the author:
- Considers the recent development of MIMO SAR technology
- Includes selected GPU implementation
- Provides a numerical analysis of system parameters (including platforms, sensor, and image focusing, and their influence)
- Explores wave-target interactions, signal transmission and reception, image formation, motion compensation
- Covers all platform motion compensation and error analysis, and their impact on final image radiometric and geometric quality
- Describes a ground-based SFMCW system
Principles of Synthetic Aperture Radar Imaging: A System Simulation Approach
is dedicated to the use, study, and development of SAR systems. The
book focuses on image formation or focusing, treats platform motion and
image focusing, and is suitable for students, radar engineers, and
microwave remote sensing researchers.
Reviews
"This book provides readers with a
comprehensive and complete description of synthetic aperture radar
principle. Its unique feature of full-blown SAR image simulations and
modeling, including sensor and target location, targets geometric and
radiometric scattering characteristics, and clutters from system and
environment, distinguishes this book from other SAR processing books.
Insightful and state of arts information on SAR trajectory, SAR focusing
and motion compensation are clearly detailed. For the first time,
satellite SAR systems, such as RadarSAT-2, TerraSAR-X and ALOS/PALSAR
and their imaging configurations, are integrated into the simulation and
modeling. One of the highlights of this book rests at image simulations
of hard targets, such as B757-200, B747-400, A321 and MD80 for various
aspect angles. These simulations make target identification possible as
illustrated using real TerraSAR-X SAR images. This book can be used both
as a reference book for SAR researchers and as a textbook for graduate
students if exercises can be supplemented."
—Jon-Sen Lee, Naval Research Laboratory (Retired), Washington DC, USA
Related/Background:
- Chen, Chia-Tang; Kun-Shan Chen; Jong-Sen Lee, "The use of fully
polarimetric information for the fuzzy neural classification of SAR
images," in Geoscience and Remote Sensing, IEEE Transactions on , vol.41, no.9, pp.2089-2100, Sept. 2003
doi: 10.1109/TGRS.2003.813494
Abstract:
Presents a method, based on a fuzzy neural network, that uses fully
polarimetric information for terrain and land-use classification of
synthetic aperture radar (SAR) image. The proposed approach makes use of
statistical properties of polarimetric data, and takes advantage of a
fuzzy neural network. A distance measure, based on a complex Wishart
distribution, is applied using the fuzzy c-means clustering algorithm,
and the clustering result is then incorporated into the neural network.
Instead of preselecting the polarization channels to form a feature
vector, all elements of the polarimetric covariance matrix serve as the
target feature vector as inputs to the neural network. It is thus
expected that the neural network will include fully polarimetric
backscattering information for image classification. With the
generalization, adaptation, and other capabilities of the neural
network, information contained in the covariance matrix, such as the
amplitude, the phase difference, the degree of polarization, etc., can
be fully explored. A test image, acquired by the Jet Propulsion
Laboratory Airborne SAR (AIRSAR) system, is used to demonstrate the
advantages of the proposed method. It is shown that the proposed
approach can greatly enhance the adaptability and the flexibility giving
fully polarimetric SAR for terrain cover classification. The
integration of fuzzy c-means (FCM) and fast generalization dynamic
learning neural network (DLNN) capabilities makes the proposed algorithm
an attractive and alternative method for polarimetric SAR
classification.
keywords: {fuzzy neural nets;image
classification;radar imaging;radar polarimetry;synthetic aperture
radar;terrain mapping;AIRSAR system;Jet Propulsion Laboratory Airborne
SAR;SAR images;complex Wishart distribution;dynamic learning neural
network;fuzzy c-means clustering algorithm;fuzzy neural network;image
classification;land-use classification;polarimetric SAR
classification;polarimetric backscattering information;polarimetric
covariance matrix;polarimetric data;polarimetric
information;polarization channels;speckle filtering;statistical
properties;synthetic aperture radar;terrain classification;terrain cover
classification;Backscatter;Clustering algorithms;Covariance
matrix;Fuzzy neural networks;Image classification;Neural
networks;Polarization;Propulsion;Synthetic aperture radar;System
testing},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1232222&isnumber=27602
Kun-Shan
Chen; Hsiu-Wen Wang; Chih-Tien Wang; Wen-Yen Chang, "A Study of Decadal
Coastal Changes on Western Taiwan Using a Time Series of ERS Satellite
SAR Images," in Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of , vol.4, no.4, pp.826-835, Dec. 2011
doi: 10.1109/JSTARS.2011.2131635
Abstract:
In this paper, coastal line changes were monitored and analyzed from a
sequence of ERS-1/2 SAR images covering the years 1996 to 2005, totaling
44 images for each year. Waterlines were extracted using a multi-scale
edge detection algorithm, and further refined by means of morphology.
Substantial analysis was carried out in conjunction with ground survey
and sonar bathymetric mapping. In addition, tidal records were used to
ensure all the shore lines been calibrated to the same tidal level.
Results showed that Waisanting Sandbar, a north-southward sandbar,
experienced significant accretion and erosion, moving southward about
700 meters during a 10-year period, and shrinking to just one third of
its 1996 size. The surrounding coastal waters and the estuary of the
Peikang River receded substantially, moving inward toward the coastal
flat. The water channel became even more heavily deposited as a result.
Finally, Haifengdao Sandbar, another sandbar, moved southward about 1.5
km, although its size remained the same from 1996 to 2005. It also
showed a clear tendency to receding inward. We conclude that satellite
remote sensing by SAR, aided by ground tidal data, bathymetric maps, and
optical images, provides an effective and efficient tool for
understanding coastal processes over large areas of coverage and long
time spans.
keywords: {bathymetry;edge detection;geophysical image
processing;oceanographic techniques;remote sensing by
radar;rivers;synthetic aperture radar;tides;time series;AD 1996 to
2005;ERS satellite SAR image;ERS-1/2 SAR image;Haifengdao
Sandbar;Peikang River estuary;Waisanting Sandbar;Western Taiwan;coastal
water region;decadal coastal line change analysis;ground tidal
data;multiscale edge detection algorithm;north-southward sandbar;optical
image analysis;sonar bathymetric mapping method;tidal record
analysis;time series;waterline region;Global Positioning System;Image
edge detection;Remote sensing;Sea measurements;Sonar measurements;Costal
change detection;SAR},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5766783&isnumber=6086909
Cheng-Yen
Chiang; Kun-Shan Chen; Chih-Tien Wang; Nien-Shiang Chou, "Feature
Enhancement of Stripmap-Mode SAR Images Based on an Optimization
Scheme," in Geoscience and Remote Sensing Letters, IEEE , vol.6, no.4, pp.870-874, Oct. 2009
doi: 10.1109/LGRS.2009.2028038
Abstract:
Based on a nonquadratic-optimization method originally proposed for
spotlight-mode SAR image reconstruction, a modification for
stripmap-mode SAR images is presented in this letter. This is done by
mathematically reformulating the projection kernel and numerically
putting it into a form that is suitable for optimization. The
performance was evaluated by measures of the target contrast and 3-dB
beamwidth using Radarsat-1 data. Results were analyzed and compared with
those using minimum-variance and multiple-signal-classification
methods. Results demonstrate that the target's features are effectively
enhanced and that the dominant scattering centers are well separated
using the proposed method. In addition, the image fuzziness is greatly
reduced, and the image fidelity is well preserved. The effectiveness of
the modification is thus validated.
keywords: {image
enhancement;image reconstruction;optimisation;radar imaging;synthetic
aperture radar;feature enhancement;multiple-signal-classification
method;nonquadratic-optimization method;stripmap-mode SAR
image;synthetic aperture radar;Feature enhancement;minimum variance
(MV);multiple signal classification (MUSIC);stripmap SAR;synthetic
aperture radar (SAR)},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5233873&isnumber=5278412
Tzeng, Y.C.; Chen, K.S., "A fuzzy neural network to SAR image classification," in Geoscience and Remote Sensing, IEEE Transactions on , vol.36, no.1, pp.301-307, Jan 1998
doi: 10.1109/36.655339
Abstract:
Recently, neural networks have been increasingly applied to remote
sensing imagery classification. The conventional neural network
classifier performs learning from the representative information within a
problem domain on a one-pixel-one-class basis; therefore, class mixture
and the degree of membership of a pixel are generally not taken into
account, often resulting in a poor classification accuracy. Based on the
framework of a dynamic learning neural network (DL), this
communications proposes a fuzzy version (FDL) based on two steps:
network representation of fuzzy logic and assignment of membership.
Comparisons between the DL and FDL are made by applying both neural
networks to SAR image classification. Experimental results show that the
FDL has faster convergence rate than that of DL. In addition, the
separability between similar classes is improved. Moreover, the
classification results match better with ground truth
keywords:
{fuzzy neural nets;geophysical signal processing;geophysical
techniques;geophysics computing;image classification;radar
imaging;remote sensing by radar;synthetic aperture
radar;SAR;classifier;dynamic learning neural network;fuzzy logic;fuzzy
neural network;fuzzy version;geophysical measurement technique;image
classification;land surface;network representation;neural net;radar
imaging;radar remote sensing;synthetic aperture radar;terrain
mapping;Convergence;Fuzzy logic;Fuzzy neural networks;Fuzzy set
theory;Fuzzy sets;Image classification;Neural networks;Remote
sensing;Space technology;Testing},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=655339&isnumber=14284
Jin Min Kuo; Chen, K.-S., "The application of wavelets correlator for ship wake detection in SAR images," in Geoscience and Remote Sensing, IEEE Transactions on , vol.41, no.6, pp.1506-1511, June 2003
doi: 10.1109/TGRS.2003.811998
Abstract:
The detection of the wake can provide substantial information about a
ship, such as its size, direction, and speed of movement. In general
though, ship-generated wakes in synthetic aperture radar images are
associated with high sea clutter, which will cause some deterioration in
the detection performance. Therefore, a wavelet correlator, based on an
orthogonal basis function, is adopted. Three highpass images -
horizontal, vertical, and diagonal direction - are generated for each
resolution scale, followed by a process to correlate among the moduli of
different scale modulus images formed from the three highpass images.
The output of the correlation process is highly representative at the
ship's wake edges. Comparisons with other methods indicate the superior
performance of the present approach, in that not only can the wakes be
detected, but their V-shaped pattern is well preserved. The second stage
involves the application of the Radon transform technique to an
estimation of the V-opening angle from the detected ship wakes.
Ship-generated wake edges are found to be the local maxima in the
wavelet transform method of several adjacent scales, and hence, the wake
edge will be enhanced in the reconstructed data. The background noise
is also greatly reduced. In particular, the process of spatial
correlation is found to be critical. Compared to a direct Radon
transform, the proposed scheme is demonstrated to be much more effective
in terms of efficiency, as well as reliability, for ship wake detection
in noisy backgrounds.
keywords: {Radon transforms;oceanographic
techniques;radar clutter;remote sensing by radar;synthetic aperture
radar;wakes;wavelet transforms;Radon transform technique;SAR
images;detection performance;highpass images;noisy
backgrounds;orthogonal basis function;sea clutter;ship wake
detection;ship-generated wakes;synthetic aperture radar images;wake
edges;wake opening angle;wavelet transform method;wavelets
correlator;Clutter;Correlators;Image edge detection;Image
resolution;Image segmentation;Marine vehicles;Radar detection;Synthetic
aperture radar;Wavelet analysis;Wavelet transforms},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1220259&isnumber=27418
Yang-Lang
Chang; Kun-Shan Chen; Bormin Huang; Wen-Yen Chang; Benediktsson, J.A.;
Chang, L., "A Parallel Simulated Annealing Approach to Band Selection
for High-Dimensional Remote Sensing Images," in Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of , vol.4, no.3, pp.579-590, Sept. 2011
doi: 10.1109/JSTARS.2011.2160048
Abstract:
In this paper a parallel band selection approach, referred to as
parallel simulated annealing band selection (PSABS), is presented for
high-dimensional remote sensing images. The approach is based on the
simulated annealing band selection (SABS) scheme which is originally
designed to group highly correlated hyperspectral bands into a smaller
subset of modules regardless of the original order in terms of
wavelengths. SABS selects sets of correlated hyperspectral bands based
on simulated annealing (SA) algorithm and utilizes the inherent
separability of different classes to reduce dimensionality. In order to
be effective, the proposed PSABS is introduced to improve the
computational performance by using parallel computing technique. It
allows multiple Markov chains (MMC) to be traced simultaneously and
fully utilizes the parallelism of SABS to create a set of SABS modules
on each parallel node. Two parallel implementations, namely the message
passing interface (MPI) cluster-based library and the open
multi-processing (OpenMP) multicore-based application programming
interface, are applied to three different MMC techniques:
non-interacting MMC, periodic exchange MMC and asynchronous MMC for
evaluation. The effectiveness of the proposed PSABS is evaluated by NASA
MODIS/ASTER (MASTER) airborne simulator data sets and airborne
synthetic aperture radar (SAR) images for land cover classification
during the Pacrim II campaign in the experiments. The results
demonstrated that the MMC techniques of PSABS can significantly improve
the computational performance and provide a more reliable quality of
solution compared to the original SABS method.
keywords: {Markov
processes;geophysical image processing;message passing;remote
sensing;simulated annealing;synthetic aperture radar;MASTER airborne
simulator;Markov chains;NASA MODIS/ASTER;OpenMP;SABS scheme;band
selection;high-dimensional remote sensing;message passing interface;open
multi-processing;parallel simulated annealing;programming
interface;synthetic aperture radar;Annealing;Correlation;Hyperspectral
sensors;Markov processes;Simulated annealing;Message passing interface
(MPI);multiple Markov chains (MMC);open multi-processing
(OpenMP);parallel simulated annealing band selection (PSABS)},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5954137&isnumber=5997340
