Department of Mathematics
Coupled segmentation and denoising/deblurring models for hyperspectral material identification
A crucial aspect of spectral image analysis is the identification of the materials present in the object or scene being imaged and to quantify their abundance in the mixture. An increasingly useful approach to extracting such underlying structure is to employ image classification and object identification techniques to compressively represent the original data cubes by a set of spatially orthogonal bases and a set of spectral signatures. Owing to the increasing quantity of data usually encountered in hyperspectral data sets, effective data compressive representation is an important consideration, and noise and blur can present data analysis problems. In this paper, we develop image segmentation methods for hyperspectral space object material identification. We also couple the segmentation with a hyperspectral image data denoising/deblurring model and propose this method as an alternative to a tensor factorization methods proposed recently for space object material identification. The model provides the segmentation result and the restored image simultaneously. Numerical results show the effectiveness of our proposed combined model in hyperspectral material identification. © 2010 John Wiley & Sons, Ltd.
Compressive representation, Deblurring, Denoising, Hyperspectral image analysis, Segmentation, Tensors
Source Publication Title
Numerical Linear Algebra with Applications
Li, Fang, Michael K. Ng, and Robert J. Plemmons. "Coupled segmentation and denoising/deblurring models for hyperspectral material identification." Numerical Linear Algebra with Applications 19.1 (2012): 153-173.