Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/69151
標題: Linear Spectral Mixture Analysis Based Approaches to Estimation of Virtual Dimensionality in Hyperspectral Imagery
作者: Chang, C.I.
Xiong, W.
Liu, W.M.
Chang, M.L.
Wu, C.C.
Chen, C.C.C.
關鍵字: Harsanyi-Farrand-Chang (HFC) method;hyperspectral signal subspace;identification by minimum error (HySime);linear spectral mixing (LSM);orthogonal subspace projection (OSP);signal subspace estimation (SSE);virtual dimensionality (VD);virtual endmember (VE);algorithm;recognition;reduction
Project: Ieee Transactions on Geoscience and Remote Sensing
期刊/報告no:: Ieee Transactions on Geoscience and Remote Sensing, Volume 48, Issue 11, Page(s) 3960-3979.
摘要: 
Virtual dimensionality (VD) is a new concept which was originally developed for estimating the number of spectrally distinct signatures present in hyperspectral data. The effectiveness of the VD is determined by the technique used for VD estimation. This paper develops an orthogonal subspace projection (OSP) technique to estimate the VD. The idea is derived from linear spectral mixture analysis where a data sample vector is modeled as a linear mixture of a finite set of what is called as virtual endmembers in this paper. A similar idea was also previously investigated by the signal subspace estimate (SSE) and was later improved by hyperspectral signal subspace identification by minimum error (HySime), where the minimum mean squared error is used as a criterion to determine the VD. Interestingly, with an appropriate interpretation, the proposed OSP technique includes the SSE/HySime as its special case. In order to demonstrate its utility, experiments using synthetic images and real image data sets are conducted for performance analysis.
URI: http://hdl.handle.net/11455/69151
ISSN: 0196-2892
DOI: 10.1109/tgrs.2010.2068552
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