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                                          首页 > 讲座报告 > 正文

                                          Hyperspectral Image Classification: A Statistical Detection Theory Approach

                                          作者:正规赌钱网时间:2019-06-12点击数:

                                          报告人 Prof.Chang Chein-I 时间 2019年6月15日9:10-9:45
                                          地点 西安市高新四路蓝溪国际酒店群贤厅

                                          报告题目:Hyperspectral Image Classification: A Statistical Detection Theory Approach

                                          报告人:IEEE Life Fellow/SPIE Fellow, Prof.Chang Chein-I

                                          报告时间:2019年6月15日9:10-9:45

                                          报告地点:西安市高新四路蓝溪国际酒店群贤厅

                                          报告摘要:

                                          This talk presents a statistical detection theory approach to hyperspectral image (HSI) classification which is quite different from many conventional approaches reported in the HSI classification literature. It interprets a multi-class classification problem as a multi-target detection problem in such a way that the well-established statistical detection theory can be readily applicable to solving classification problems. In particular, it introduces two types of classification,a prioriclassification anda posterioriclassification, which can be considered as counterparts of Bayes detection and maximuma posteriori(MAP) detection respectively in detection theory. Accordingly, detection probability and false alarm probability can be also translated to classification rate and false classification rate derived from a confusion classification matrix for classification. To evaluate the effectiveness ofa posterioriclassification a newa posterioriclassification measure, to be called precision rate (PR), is also introduced by MAP classification in contrast to overall accurate (OA) that has been used for Bayes classification. The experimental results provide evidence thata prioriclassifier as Bayes classifier which performs well in terms of OA does not necessarily perform well asa posterioriclassifier in terms of PR. That is, PR is the only criterion that can be used asa posterioriclassification measure to evaluate how well a classifier performs.

                                          报告人简介:

                                          Chang Chein-I is a Professor with Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC) and is currently holding a Chang Jiang Scholar Chair Professorship at Dalian Maritime University awarded by Ministry of Education, China.

                                          Dr, Chang has piublsihed over 175 referred SCI publications including more than 60 journal articles inIEEE Transaction on Geoscience and Remote Sensingand has seven patents with several pending on hyperspectral image processing. He authored three books,Hyperspectral Imaging: Techniques for Spectral Detection and Classification(Kluwer Academic Publishers, 2003),Hyperspectral Data Processing:Algorithm Design and Analysis(Wiley, 2013),Real Time Progressive Hyperspectral Image Processing: Endmember Finding and Anomaly Detection(Springer, 2016) andReal-Time Recursive Hyperspectral Sample and Band Processing: Algorithm Architecture and Implementation, 2017. In addition, He edited two books,Recent Advances in Hyperspectral Signal and Image Processing(Trasworld Research Network, India, 2006) andHyperspectral Data Exploitation: Theory and Applications(John Wiley & Sons, 2007) and co-edited with A. Plaza a book onHigh Performance Computing in Remote Sensing(CRC Press, 2007). He has more than 20100 google SCI citation with high index 63.

                                          Dr. Chang has received his Ph.D. in Electrical Engineering from University of Maryland, College Park. He is a Life Fellow of IEEE and SPIE with contributions to hyperspectral image processing.

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