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Professor Chein-I Chang, IEEE Fellow, SPIE Fellow 

                                  






EDUCATION

Ph.D., Electrical Engineering, May 1987, University of Maryland, College Park, Maryland       

Dissertation, A Generalized Minimax Approach to Statistical Decision Problems with Applications to Rate-Distortion Theory directed by Dr. Lee D. Davisson

M.S., Electrical Engineering, August 1982, University of Illinois, Urbana-Champaign, IL

Thesis, Memory Length Effects in the Detection of Discrete-Time Signals in Stationary Gaussian Noise, directed by Dr. H. Vincent Poor

M.S., Theoretical Computer Science, May 1980, University of Illinois, Urbana-Champaign, IL

M.A., Mathematics, February 1977, State University of New York at Stony Brook, New York

M.S., Mathematics, May 1975, National Tsing Hua University, Taiwan, Republic of China

 Thesis, Asymptotic Distribution in Zg and Independence of Sequence of Zg directed by Drs. Ta Chung Lee and Peter Shiue.

B.S., Mathematics, May 1973, Soochow University, Taipei, Taiwan, Republic of China

EMPLOYMENT AND WORK EXPERIENCE

7/01 - present    Professor of Electrical Engineering, University of Maryland, Baltimore County, MD.

8/11 – 7/13          Chair Professor of Remote Sensing Technology, National Chung Hsing University, Taiwan, ROC.

2/09 – 1/12          Distinguished Professor, Providence University, Taichung, Taiwan, ROC.

8/09 – 7/10          Distinguished Visiting Fellow/Fellow Professor, National Science Council in Taiwan, ROC.

8/06 – 7/09          Chair Professor of Disaster Reduction Technology, Environmental Restoration and Disaster Reduction Research Center, National Chung Hsing University, Taiwan, ROC.

8/05 – 7/06          Distinguished Lecture Chair, Ministry of Education, Taiwan, ROC.

6/02 – 9/03          Senior Research Associate, June 2002-September 2003, National Research Council (NRC) sponsored by the US Army Soldier and Biological Chemical Command, Edgewood Chemical and Biological Center, Aberdeen Proving Ground, Maryland.         

7/93 - 6/01           Associate Professor of Electrical Engineering, University of Maryland, Baltimore County, MD

8/94 - 7/95           Visiting Research Specialist, Institute of Information Engineering,  National Cheng Kung University, Tainan, Taiwan, Republic of China

8/87 - 6/93           Assistant Professor of Electrical Engineering, University of Maryland Baltimore County

1/87 - 8/87           Visiting Assistant Professor of Electrical Engineering, University of Maryland Baltimore County

RESEARCH INTERESTS (Google Scholar Profiles)

  • Remote Sensing Signal/Image Processing, Automatic Target Recognition, Medical Imaging 

Graduates

  • 39 Ph.D.'s and 40 M.S.'s

169 refereed journal articles

  • 54 IEEE Transaction on Geoscence and Remote Sensing. 
  • 16 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • 8   IEEE Transaction on Geoscence and Remote Sensing Letters
    5   IEEE Transaction on Information Theory
    18 other IEEE Transaction Journals. 
    12 Optical Engineering
    5   Pattern Recognition

  • 221 Conference presentations and papers
  • 16 book chapters

Patents

  • 4 awarded and 3 pending

Books

1

C.-I Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic/Plenum Publishers, New York, N.Y., ISBN 0-306-47483-2, p. 374 pages, 2003.

Preface: preface.pdf 

This book is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. In particular, some known techniques, such as OSP (Orthogonal Subspace Projection) and CEM (Constrained Energy Minimization) that were previously developed in the RSSIPL, are discussed in great detail. This book is self-contained and can serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.

2

C.-I Chang, Hyperspectral Data Processing: Algorithm Design and Analysis, John Wiley & Sons, ISBN  978-0-471-69056-6, April, 2013.

Preface: preface_final.pdf

Hyperspectral Data Processing: Algorithm Design and Analysis is a culmination of the research conducted in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. Specifically, it treats hyperspectral image processing and hyperspectral signal processing as separate subjects in two different categories. Most materials covered in this book can be used in conjunction with the author’s first book, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, without much overlap.

Many results in this book are either new or have not been explored, presented, or published in the public domain. These include various aspects of endmember extraction, unsupervised linear spectral mixture analysis, hyperspectral information compression, hyperspectral signal coding and characterization, as well as applications to conceal target detection, multispectral imaging, and magnetic resonance imaging. Hyperspectral Data Processing contains eight major sections:

Part I: provides fundamentals of hyperspectral data processing
Part II: offers various algorithm designs for endmember extraction
Part III: derives theory for supervised linear spectral mixture analysis
Part IV: designs unsupervised methods for hyperspectral image analysis
Part V: explores new concepts on hyperspectral information compression
Parts VI & VII: develops techniques for hyperspectral signal coding and characterization
Part VIII: presents applications in multispectral imaging and magnetic resonance imaging

Hyperspectral Data Processing compiles an algorithm compendium with MATLAB codes in an appendix to help readers implement many important algorithms developed in this book and write their own program codes without relying on software packages.

Hyperspectral Data Processing is a valuable reference for those who have been involved with hyperspectral imaging and its techniques, as well those who are new to the subject.

3C.-I Chang, Real-Time Progressive Hyperspectral Image Processing: Endmember Finding and Anomaly Detection, Springer, ISBN: 978-1-4419-6187-7, March 2016. 

The book covers the most crucial parts of real-time hyperspectral image processing: causality and real-time capability. Recently, two new concepts of real time hyperspectral image processing, Progressive Hyperspectral Imaging (PHSI) and Recursive Hyperspectral Imaging (RHSI). Both of these can be used to design algorithms and also form an integral part of real time hyperpsectral image processing. This book focuses on progressive nature in algorithms on their real-time and causal processing implementation in two major applications, endmember finding and anomaly detection, both of which are fundamental tasks in hyperspectral imaging but generally not encountered in multispectral imaging. This book is written to particularly address PHSI in real time processing, while a book, Recursive Hyperspectral Sample and Band Processing: Algorithm Architecture and Implementation (Springer 2016) can be considered as its companion book.

Includes preliminary background which is essential to those who work in hyperspectral imaging area
Develops sequential and progressive algorithms for finding endmembers as they relate to real time hyperspectral image processing
Designs algorithms for anomaly detection from causality and real time perspectives and investigates the effects of causality and real-time processing in anomaly detection

4

C.-I Chang, Real-Time Recursive Hyperspectral Sample and Band Processing: Algorithm Architecture and Implementation, Springer, ISBN: 978-3-319-45170-1, April 2017.

This book explores recursive architectures in designing progressive hyperspectral imaging algorithms. In particular, it makes progressive imaging algorithms recursive by introducing the concept of Kalman filtering in algorithm design so that hyperspectral imagery can be processed not only progressively sample by sample or band by band but also recursively via recursive equations. This book can be considered a companion book of author’s books, Real-Time Progressive Hyperspectral Image Processing, published by Springer in 2016.

Explores recursive structures in algorithm architecture
Implements algorithmic recursive architecture in conjunction with progressive sample and band processing
Derives Recursive Hyperspectral Sample Processing (RHSP) techniques according to Band-Interleaved Sample/Pixel (BIS/BIP) acquisition format
Develops Recursive Hyperspectral Band Processing (RHBP) techniques according to Band SeQuential (BSQ) acquisition format for hyperspectral data


Edited books

1

Chein-I Chang, Ed., Recent Advances in Hyperspectral Signal and Image Processing, Research Signpost, Trasworld Research Network, India, 2006

Preface: Preface_new.pdf Frontcover: cover.pdf.pdf Brochure: brochure.pdf

This book is a collection of 16 chapters written by leading experts in their respective areas. It is made up of two parts, Part I: 6 chapters contributed by the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, and Part II: 10 chapters contributed by researchers from other organizations. The chapters presented in the book offer readers with a broad view of hyperspectral signal and image processing. The book will serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.

2

Chein-I Chang, Ed., Hyperspectral Data Exploitation: Theory and Applications, John Wiley & Sons, 2007

Overview: c010.pdf    Frontcover: frontcover_HDE.pdf

Authored by a panel of experts in the field, this book focuses on hyperspectral image analysis, systems, and applications. With discussion of application-based projects and case studies, this professional reference will bring you up-to-date on this pervasive technology, wether you are working in the military and defense fields, or in remote sensing technology, geoscience, or agriculture.Authored by a panel of experts in the field, this book focuses on hyperspectral image analysis, systems, and applications. With discussion of application-based projects and case studies, this professional reference will bring you up-to-date on this pervasive technology, wether you are working in the military and defense fields, or in remote sensing technology, geoscience, or agriculture.

3

A. Plaza and Chein-I Chang, Ed., High Performance Computing in Remote Sensing, Chapman & Hall/CRC Press, 2007.

cover:C6625_Cover.pdf  Preface: Chapter01_preface_finished.pdf

The recent use of latest-generation sensors in airborne and satellite platforms is producing a nearly continual stream of high-dimensional data, which, in turn, is creating new processing challenges. To address the computational requirements of time-critical applications, researchers have begun incorporating high performance computing (HPC) models in remote sensing missions. High Performance Computing in Remote Sensing is one of the first volumes to explore state-of-the-art HPC techniques in the context of remote sensing problems. It focuses on the computational complexity of algorithms that are designed for parallel computing and processing.

The book first addresses key computing concepts and developments in remote sensing. It also covers application areas not necessarily related to remote sensing, such as multimedia and video processing. Each subsequent chapter illustrates a specific parallel computing paradigm, including multiprocessor (cluster-based) systems, large-scale and heterogeneous networks of computers, grid computing platforms, and specialized hardware architectures for remotely sensed data analysis and interpretation.

The extensive reviews of current and future developments combined with thoughtful perspectives on the potential challenges of adapting HPC paradigms to remote sensing problems will undoubtedly foster collaboration and development among many fields.



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