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Band Selection for Hyperspectral Image Classification
时间:2018-08-13 22:07    点击:   所属单位:物理与光电工程学院
讲座名称 Band Selection for Hyperspectral Image Classification
讲座时间 2018-08-15 09:10:00
讲座地点 北校区图书馆西裙楼三楼报告厅
讲座人 Prof. Chein-I Chang
讲座人介绍

Chein-I Chang received his Ph.D. in Electrical Engineering from the University of Maryland, College Park and is currently a Professor with Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County. He is also holding a Chang Jiang Scholar Chair Professor at the Dalian Maritime University. He established a Remote Sensing Signal and Image Processing Laboratory, and conducts research in designing and developing image processing algorithms for hyperspectral imaging at UMBC and a Center for Hyperspectral Imaging in Remote Sensing in Dalian Maritime University,. His broad research experience has led to over 170 peer refereed journal articles with more than 60 papers published in IEEE Transaction one Geoscience and Remote Sensing. Dr. Chang authors four books, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Hyperspectral Data Processing: Algorithm Design and Analysis, Real Time Progressive Hyperspectral Image Processing: Endmember Finding and Anomaly Detection and Real-Time Recursive Hyperspectral Sample and Band Processing: Algorithm Architecture and Implementation. In addition, he will also edit two books on Hyperspectral Data Exploitation and Recent Advances in Hypersspectral Signal and Image Processing. Dr. Chang is elected an SPIE fellow for his achievements in hyperspectral image processing and co-edited one book, Higher Performance Computing in Remote Sensing with Antonio Plaza. Dr. Chang also received an NRC (National Research Council) senior research associateship sponsored by the US Army Soldier and Biological Command, Edgewood Chemical Biological Center (ECBC). Dr. Chang is a Life Fellow of IEEE and a Fellow of SPIE.

讲座内容

Hyperspectral image classification has received considerable interest. Due to use of hundreds of contiguous spectral bands, not all bands are useful for classification. Accordingly, judiciously selecting appropriate bands at different wavelengths that properly respond to different classes material substances is crucial. This talk explores the utility of band selection (BS) in hyperspectral classification and further discusses recent approaches to BS with applications in hyperspectral image classification.

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