Hyperspectral image processing using locally linear embedding精编.pdf

University of Pennsylvania Scholarly Commons Departmental Papers (BE) Department of Bioengineering arch 2003 Hyperspectral Image Processing Using Locally Linear Embedding David H. Kim University of P ennsylvania Leif H. Finkel University of P ennsylvania, leif@ Follow this and additional works at : /be_papers Recommended Citation Kim, D. H., Finkel, L. H. (2003). Hyperspectral Image Processing Using Locally Linear Embedding. Retrieved from /be_papers/8 Copyright 2003 IEEE. Reprinted from Proceedings of the 1st International IEEE EMBS Conference on Neural Eng ineering 2003, pages 316-319. Publisher URL : /xpl/tocresult.jsp ?isNumber=26900page=5 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvanias products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. This paper is posted at ScholarlyCommons. /be_papers/8 For more information, please contact repository@pobox .. Hyperspectral Image Processing Using Locally Linear Embedding Abstract We describe a method of processing hyperspectral images of natural scenes that uses a combination of k- means clustering and locally linear embedding (LLE). The primary goal is to assist anomaly detection by preserving spectral uniqueness among the pixels. In order to reduce redundancy among the p