内容简介：

Hyperspectral imaging has been of great interest in remote sensing and Earth observations due to the fact that its high spectral resolution offers powerful discriminant capability in separating objects or materials with subtle spectral discrepancy. However, the resulting high spectral dimensionality may bring out difficulty in data processing and analysis. Extensive research on hyperspectral image classification with labeled samples can be found in the literature. However, unsupervised classification through clustering is more challenging. Traditional clustering techniques, such as k-means clustering, may not work well for high-dimensional data because distance measurement becomes less accurate in such a case. Nonnegative matrix factorization (NMF) has been widely used in hyperspectral image analysis. As an unsupervised technique, it has been used as a preprocessing step for dimensionality reduction or classification. In this talk, we focus on the orthogonal nonnegative matrix factorization (ONMF), where the orthogonal constraint can help reinforce the sparseness in data factorization. The kernel version of ONMF (KONMF) is developed, and the spectral clustering is applied to a factor matrix to achieve clustering. An approximate kernel version is also developed to reduce the computational cost of KONMF. Compared to the state-of-the-art subspace clustering approaches, the KONMF can offer much higher classification accuracy.

主讲人简介：

Dr. Du is Bobby Shackouls Professor with the Department of Electrical and Computer Engineering, Mississippi State University, USA. Her research interests include hyperspectral remote sensing image analysis and applications, pattern recognition, and machine learning. Dr. Du is a Fellow of IEEE and SPIE—International Society for Optics and Photonics. She served as Co-Chair for the Data Fusion Technical Committee of IEEE Geoscience and Remote Sensing Society (GRSS) in 2009–2013, and Chair for Remote Sensing and Mapping Technical Committee of International Association for Pattern Recognition (IAPR) in 2010–2014. She served as Associate Editor for IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2011–2015), IEEE Signal Processing Letters (2012–2015), and Journal of Applied Remote Sensing (2014–2015). Currently, Dr. Du is the Editor-in-Chief of IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS). She was the Guest Editor of several special issues published in IEEE Transactions on Geoscience and Remote Sensing, IEEE JSTARS, Journal of Applied Remote Sensing, Pattern Recognition Letters, Remote Sensing, Sensors. Dr. Du is the General Chair of the 4th IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) in 2012, and the General Chair of the 7th and 8^th IAPR Workshop on Pattern Recognition in Remote Sensing in 2012 and 2014, respectively.

主办单位：人力资源部教师发展中心

承办单位：信息与通信工程学院