讲座题目：基于时-空-谱深度学习框架的遥感影像缺失信息重建 主讲人：袁强强 教授 开始时间：2019-11-30 16:40:00 讲座地址：中北校区办公楼小礼堂 主办单位：计算机科学与技术学院
报告人简介： 袁强强教授，国家优秀青年科学基金获得者，武汉大学测绘学院，2014年度“香江学者”计划获得者，武汉大学“珞珈青年学者”。主要从事遥感影像质量改善与信息融合等方面的研究工作，主持国家重点研发计划课题、自然科学基金、博士后特别资助等基金多项。曾获“湖北省优秀博士论文”、武汉大学“十大学术之星”、测绘科技进步一等奖（2次）、湖北省自然科学二等奖等。现担任IEEE Access等三种国际期刊的副主编、编委或客座编辑，以及IEEE TIP、IEEE TGRS等30余种国际期刊的审稿人。至今在IEEE TIP，IEEE TGRS，IEEE TCSVT等国内外学术期刊上发表论文40余篇，2篇论文入选ESI热点论文，7篇论文入选ESI高被引论文，谷歌学术引用2298次。 报告内容： Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i.e., the data usability is greatly reduced. In this paper, a novel method of missing information reconstruction in remote sensing images is proposed. The unified spatial-temporal-spectral framework based on a deep convolutional neural network (CNN) employs a unified deep CNN combined with spatial-temporal-spectral supplementary information. In addition, to address the fact that most methods can only deal with a single missing information reconstruction task, the proposed approach can solve three typical missing information reconstruction tasks: (1) dead lines in Aqua Moderate Resolution Imaging Spectroradiometer band 6; (2) the Landsat Enhanced Thematic Mapper Plus scan line corrector-off problem; and (3) thick cloud removal. It should be noted that the proposed model can use multisource data (spatial, spectral, and temporal) as the input of the unified framework. The results of both simulated and real-data experiments demonstrate that the proposed model exhibits high effectiveness in the three missing information reconstruction tasks listed above. |
