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| Kun-Shan CHEN |
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
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
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