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

Fang Li; Michael K. Ng; Robert Plemmons; Sudhakar Prasad; Qiang Zhang

doi:10.1117/12.850121

15 April 2010 Hyperspectral image segmentation, deblurring, and spectral analysis for material identification

Fang Li, Michael K. Ng, Robert Plemmons, Sudhakar Prasad, Qiang Zhang

Proceedings Volume 7701, Visual Information Processing XIX; 770103 (2010) https://doi.org/10.1117/12.850121
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Fang Li, Michael K. Ng, Robert Plemmons, Sudhakar Prasad, and Qiang Zhang "Hyperspectral image segmentation, deblurring, and spectral analysis for material identification", Proc. SPIE 7701, Visual Information Processing XIX, 770103 (15 April 2010); https://doi.org/10.1117/12.850121

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available