Title :
Improved covariance matrices for point target detection in hyperspectral data
Author :
Sofer, Y. ; Geva, E. ; Rotman, S.R.
Author_Institution :
Dep. of Electr. & Comput. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel
Abstract :
Algorithms for point target detection in hyperspectral images use the inverse covariance matrix in order to separate a detected pixel from it surrounding noise. The inverse covariance matrix can be implemented from all the pixels or from the close surroundings of the examined pixel. We compare the different methods and conclude which method brings the best results.
Keywords :
covariance matrices; geophysical image processing; remote sensing; hyperspectral data; hyperspectral images; inverse covariance matrix; point target detection; Background noise; Biomedical imaging; Covariance matrix; Eigenvalues and eigenfunctions; Histograms; Hyperspectral imaging; Matched filters; Object detection; Pixel; Signal to noise ratio;
Conference_Titel :
Microwaves, Communications, Antennas and Electronics Systems, 2009. COMCAS 2009. IEEE International Conference on
Conference_Location :
Tel Aviv
Print_ISBN :
978-1-4244-3985-0
DOI :
10.1109/COMCAS.2009.5385980
