Exploiting the Low-dimensional Structures for Hyperspectral Image Processing and Analysis
讲座名称：
Exploiting the Low-dimensional Structures for Hyperspectral Image Processing and Analysis
讲座时间：
2018-06-15
讲座人：
张洪艳
形式：
校区：
兴庆校区
实践学分：
讲座内容：
应电信学院信通系李杰副教授、齐春教授邀请，武汉大学测绘遥感信息工程国家重点实验室青年长江学者张洪艳教授，广州大学骆仁波博士于2018年6月15日来访我院并作学术讲座。
讲座题目：Exploiting the Low-dimensional Structures for Hyperspectral Image Processing and Analysis
讲座地点：电信学院西一楼第一会议室
讲座时间：2018年6月15日9:00
讲座人：张洪艳
讲座摘要：
Hyperspectral remote sensing images are a typical kind of high dimensional data with low dimensional structures. This talk explores the low-dimensional structures of the hyperspectral remote sensing image with the sparse representation and low rank models. Under the the sparse representation framewok, the discriminability of the hyperspectral remote sensing signals can be enhanced in the representation domain with better land object recognition. With the low rank matrix recovery model, the clean hyperspectral image is exploited for the denoising and unmixing tasks under the corruption of various types of noise generated in the image acquisition procedure.
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