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Super-resolution Image via Hyperspectral and Multispectral Data Fusion using Big-data Convex Optimization
时间:2018-10-23 15:24    点击:   所属单位:通信工程学院
讲座名称 Super-resolution Image via Hyperspectral and Multispectral Data Fusion using Big-data Convex Optimization
讲座时间 2018-11-01 10:00:00
讲座地点 北校区新科技楼 1012会议室
讲座人 Prof. Chong-Yung Chi
讲座人介绍 Chong-Yung Chi (祁忠勇) received the Ph.D. degree in Electrical Engineering from the University of Southern California, Los Angeles, California, in 1983. From 1983 to 1988, he was with the Jet Propulsion Laboratory, Pasadena, California. He has been a Professor with the Department of Electrical Engineering since 1989 and the Institute of Communications Engineering (ICE) since 1999 (also the Chairman of ICE during 2002-2005),  Tsing Hua University, Hsinchu, Taiwan, China. He has published more than 230 technical papers including more than 90 journal papers (mostly in IEEE Trans. Signal Processing), and a new textbook, Convex Optimization for Signal Processing and Communications from Fundamentals to Applications, CRC Press, 2017 (popularly used in an invited intensive short course more than 15 times in major universities in China since 2010). His current research interests include signal processing for wireless communications, convex analysis and optimization for blind source separation, biomedical and hyperspectral image analysis. He has served as Associate editors for 4 IEEE journals, especially IEEE TSP for 9 years. Currently, he is a member of Sensor Array and Multichannel Technical Committee, IEEE Signal Processing Society. Dr. Chi is a senior member of IEEE.
讲座内容 Direct acquisition of hyperspectral and high-spatial-resolution (HSR) image data (target images) with remote-sensing sensors is expensive, while such images play an essential role in accurate identification/classification of the underlying materials in the scene of interest. In this talk, we present a newly invented convex optimization based coupled non-negative matrix factorization (CO-CNMF) algorithm to fuse two different datasets, one captured by HSR multispectral sensor and the other by low-spatial-resolution (LSR) hyperspectral sensor. With sparsity-promoting l1-norm regularization and simplex (of materials’ spectral signatures) volume-demoting regularization, this algorithm solves a large-scale bi-convex problem, by the alternating direction method of multipliers (ADMM), where two non-negative matrix factorizations are intrinsically coupled by a low-rank model of the target image, one for extracting the spectral information from the LSR hyperspectral image and the other for extracting the spatial information from the HSR multispectral image. The CO-CNMF algorithm can be shown to converge to a stationary-point solution together with a carefully designed ADMM that are practically applicable to fusion of million-scale datasets. Finally, experiments designed based on the Wald’s protocol are presented to demonstrate that the CO-CNMF algorithm yields much superior fusion performance over six benchmark super-resolution methods on ROSIS, HYDICE and AVIRIS datasets, in terms of several performance measures including peak signal-to-noise ratio (PSNR) and root mean square error (RMSE), with lower or comparable computational load.
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