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Conference Paper: Hyperspectral image segmentation, deblurring, and spectral analysis for material identification
Title | Hyperspectral image segmentation, deblurring, and spectral analysis for material identification |
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Authors | |
Keywords | Deblurring Denoising Dimensionality reduction Spectral mixture analysis Segmentation Hyperspectral data Classification |
Issue Date | 2010 |
Citation | Proceedings of SPIE - The International Society for Optical Engineering, 2010, v. 7701, article no. 770103 How to Cite? |
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. © 2010 SPIE. |
Persistent Identifier | http://hdl.handle.net/10722/276879 |
ISSN | 2023 SCImago Journal Rankings: 0.152 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Li, Fang | - |
dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Plemmons, Robert | - |
dc.contributor.author | Prasad, Sudhakar | - |
dc.contributor.author | Zhang, Qiang | - |
dc.date.accessioned | 2019-09-18T08:34:55Z | - |
dc.date.available | 2019-09-18T08:34:55Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | Proceedings of SPIE - The International Society for Optical Engineering, 2010, v. 7701, article no. 770103 | - |
dc.identifier.issn | 0277-786X | - |
dc.identifier.uri | http://hdl.handle.net/10722/276879 | - |
dc.description.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. © 2010 SPIE. | - |
dc.language | eng | - |
dc.relation.ispartof | Proceedings of SPIE - The International Society for Optical Engineering | - |
dc.subject | Deblurring | - |
dc.subject | Denoising | - |
dc.subject | Dimensionality reduction | - |
dc.subject | Spectral mixture analysis | - |
dc.subject | Segmentation | - |
dc.subject | Hyperspectral data | - |
dc.subject | Classification | - |
dc.title | Hyperspectral image segmentation, deblurring, and spectral analysis for material identification | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1117/12.850121 | - |
dc.identifier.scopus | eid_2-s2.0-78049389202 | - |
dc.identifier.volume | 7701 | - |
dc.identifier.spage | article no. 770103 | - |
dc.identifier.epage | article no. 770103 | - |
dc.identifier.isi | WOS:000285051200002 | - |
dc.identifier.issnl | 0277-786X | - |