A. D. Stocker and A. P. Schaum, "Application of stochastic mixing models to hyperspectral detection problems," Proc. SPIE 3071, pp. 47--60, 1997. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Clustering to Improve Matched Filter Detection of Weak .. - Funk, Theiler.. (2000) (Correct)
....of is substantial. Since all eigenvalues of the estimated are above a fixed saturation level, it follows that the eigenvalues of the estimated are below a fixed saturation value. This makes the estimate of more robust and has been shown to improve the performance of the resulting matched filter [19]. We implement both the CMF and the CMFsat filters in this paper, and show that they can both be improved by clustering the image. D. Matched Filtering and means Clustering In this section, we extend the CMF to incorporate a clustered image with a set of classes. For each class a separate mean ....
A. D. Stocker and A. P. Schaum, "Application of stochastic mixing models to hyperspectral detection problems," Algorithms Multispectral Hyperspectral Imag., vol. 3071, 1997.
Co-design of Software and Hardware to Implement.. - Theiler, Frigo.. (2001) (Correct)
No context found.
A. D. Stocker and A. P. Schaum, "Application of stochastic mixing models to hyperspectral detection problems," Proc. SPIE 3071, pp. 47--60, 1997.
A Contiguity-Enhanced K-Means Clustering Algorithm for.. - Theiler, Gisler (1997) (4 citations) (Correct)
No context found.
A. D. Stocker and A. P. Schaum, "Application of stochastic mixing models to hyperspectral detection problems," in Algorithms for Multispectral and Hyperspectral Imagery III, A. Iverson and S. S. Shen, eds., Proc SPIE 3071, 1997.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC