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10 October 2008 On the performance of virtual dimensionality estimation for hyperspectral image analysis
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Abstract
The concept of virtual dimensionality (VD) has been developed for estimating the number of spectrally distinctive signals in a hyperspectral image. It has important applications in hyperspectral image analysis. For instance, it is related to the number of classes in classification and the number of endmembers in linear mixture analysis; an appropriate VD estimate will facilitate the related algorithm implementation and improve their performance. In this paper, we will evaluate several VD estimation approaches, including a Neyman-Pearson Detection based method and a Signal Subspace Estimation based method. In particular, we will discuss how the noise estimation affects the accuracy of VD estimate.
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Narreenart Raksuntorn and Qian Du "On the performance of virtual dimensionality estimation for hyperspectral image analysis", Proc. SPIE 7109, Image and Signal Processing for Remote Sensing XIV, 71090E (10 October 2008); doi: 10.1117/12.800249; https://doi.org/10.1117/12.800249
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