講座題目：基于遙感應用的圖像融合技術概述

                            Concept of Image Fusion in Remote Sensing Applications

　　主 講 人：Nicolas H. Younan

         美國密西西比州立大學教授，密西西比州立大學電子與計算機系主任

　　時   間：2018年8月21日 10:00

　　地   點：中關村校區(qū) 信息科學實驗樓202報告廳

　　主辦單位：研究生院,、信息與電子學院

　　報名方式：掃描下方二維碼

　【主講人簡介】

  Nicolas H. Younan 是密西西比州立大學電子與計算機系的系主任和James Worth Bagley主席,。他分別于 1982年1984年從密西西比州立大學獲得本科和碩士學位，1988年從俄亥俄大學獲得博士學位,。他的主要研究 方向包括信號處理和模式識別,，尤其是在遙感圖像處理應用，圖像融合,，特征提取和分類,，自動目標識別以 及數據挖掘。他發(fā)表200余篇期刊和會議論文,，他是美國IEEE協(xié)會的高級會員和IEEE GRSS協(xié)會的會員,。作為 以下兩個技術委員會委員：圖像分析和數據融合，地球信息科學,。他同時也是國際遙感模式識別協(xié)會副主席。

   Nicolas H. Younan is currently the Department Head and James Worth Bagley Chair of Electrical and Computer Engineering at Mississippi State University (MSU) . He received the B.S. and M.S. degrees from MSU in 1982 and 1984, respectively, and the Ph.D. degree from Ohio University in 1988. His research interests include signal processing and pattern recognition. He has been involved in the development of advanced image processing and pattern recognition techniques for remote sensing applications, image/data fusion, feature extraction and classification, automatic target recognition/identification, and image information/data mining. He has published over 200 papers in refereed journals and conference proceedings. He is a senior member of IEEE and a member of the IEEE Geoscience and Remote Sensing society, serving on two technical committees: Image Analysis and Data Fusion and Earth Science Informatics. He also served as the Vice Chair of the International Association on Pattern Recognition (IAPR) Technical Committee 7 on Remote Sensing.

【講座摘要】

       對地觀測衛(wèi)星提供遙感數據具有豐富的空間，光譜和時序特征,。為了更加充分利用這些信息,，很多圖像融合 的方法已經被提出。圖像融合主要指同時利用兩個甚至更多的圖像去改進圖像質量,。融合之后的圖像具有更 為豐富的信息且為改善圖像分析提供幫助,。比如，圖像融合在分類,、分割等方面可以帶來比單個圖像更好的 效果,。本次報告主要回顧目前經典的在遙感應用中的圖像融合方法。

   Earth observation satellites provide data covering different parts of the electromagnetic spectrum at different spatial, spectral, and temporal resolutions. To utilize these different types of image data effectively, a number of image fusion techniques have been developed. Image fusion is the set of methods, tools, and means of using data from two or more different images to improve the quality of the information. The fused image has rich information that will improve the performance of image analysis algorithms. This increase in quality of the information leads to better processing (ex: classification, segmentation) accuracies compared to using the information from one type of data alone. An investigation into the use of various concepts of image fusion in remote sensing applications will be presented.