Statistical Hyperspectral Target and Anomaly Detection

Chein-I Chang, PhD, earned his doctoral degree in electrical engineering at the University of Maryland, College Park, USA. Dr. Chang is currently a professor with the University of Maryland, Baltimore County (UMBC). He has been a Chang Jiang Scholar Chair Professor awarded by Ministry of Education, China since 2016 and also a Yushan Scholar Chair Professor awarded by Ministry of Education, Taiwan at National Cheng Kung University from 2022-2028, a Life-time Chair Professor of National Yang Ming Chiao Tung University, Hsinchu, Taiwan since 2025. He was the director of the Center for Hyperspectral Imaging in Remote Sensing (CHIRS) at Dalian Maritime University, Dalian, China since 2016.
Dr. Chang authored four books, Hyperspectral Imaging: Techniques for Spectral Detection and Classification published by Kluwer Academic Publishers, 2003, Hyperspectral Data Processing: Algorithm Design and Analysis, John Wiley & Sons, 2013, Real Time Progressive Hyperspectral Image Processing: Endmember Finding and Anomaly Detection published by Springer, 2016, Recursive Hyperspectral Sample and Band Processing: Algorithm Architecture and Implementation, published by Springer, 2017. In addition, Dr. Chang has edited three books, Recent Advances in Hyperspectral Signal and Image Processing published by Trivandrum, Kerala, India, 2006, Hyperspectral Data Exploitation: Theory and Applications, published by John Wiley & Sons, 2007, Advances in Hyperspectral Image Processing Techniques, published by Wiley, 2023. He also co-edited, with A. Plaza, a book entitled High Performance Computing in Remote Sensing, published by CRC Press, 2007 and two special issue books from MDPI.
Dr. Chang is a Life Fellow at the Institute of Electrical and Electronics Engineers, a Fellow of the SPIE (Society for Photo-optical Instrumentation Engineers) and a Fellow of the AAIA (Asia-Pacific Artificial Intelligence Association). He is currently serving as associate editor of Remote Sensing and IEEE Transaction on Geoscience and Remote Sensing.