Hyperspectral image segmentation, deblurring, and spectral analysis for material identification

An important aspect of spectral image analysis is identification of materials present in the object or scene being imaged. Enabling technologies include image enhancement, segmentation and spectral trace recovery. Since multi-spectral or hyperspectral imagery is generally low resolution, it is possible for pixels in the image to contain several materials. Also, noise and blur can present significant data analysis problems. In this paper, we first describe a variational fuzzy segmentation model coupled with a denoising/deblurring model for material identification. A statistical moving average method for segmentation is also described. These new approaches are then tested and compared on hyperspectral images associated with space object material identification.

• Type: Conference
• Year: 2010
• Where: SPIEVIP
• Authors: Fang Li, Michael K. Ng, Robert J. Plemmons, Sudhakar Prasad, Qiang Zhang
• Keywords: Computer Graphics, Material Identification, Spectral Image Analysis, SPIEVIP 2010, Variational Fuzzy Segmentation