DocumentCode :
77749
Title :
An Antinoise Method for Hyperspectral Unmixing
Author :
Chunzhi Li ; Aimin Zhou ; Guixu Zhang ; Faming Fang
Author_Institution :
Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai, China
Volume :
12
Issue :
3
fYear :
2015
fDate :
Mar-15
Firstpage :
636
Lastpage :
640
Abstract :
In this letter, we propose an antinoise method for hyperspectral unmixing. In the antinoise method, all noises are addressed. The following techniques are applied: 1) an endmember dictionary is constructed first to initialize the solution; 2) an approximated L0 norm constraint is employed to prune the dictionary and fulfill the sparse coding; and 3) the Itakura-Saito divergence, instead of the Square of Euclidean Distance divergence, is utilized to construct a novel optimization function. The experimental results on both synthetic and real hyperspectral data sets demonstrate the efficacy of the proposed method.
Keywords :
geophysical image processing; geophysical signal processing; geophysical techniques; hyperspectral imaging; Itakura-Saito divergence; Square of Euclidean Distance divergence; antinoise method; approximated L0 norm constraint; endmember dictionary; hyperspectral image analysis; hyperspectral unmixing; optimization function; real hyperspectral data set; sparse coding; synthetic hyperspectral data set; Dictionaries; Encoding; Estimation; Hyperspectral imaging; Noise; Antinoise method; Itakura–Saito (IS) divergence; Itakura??Saito (IS) divergence; dictionary pruning; sparse coding; spectral unmixing (SU);
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
Publisher :
ieee
ISSN :
1545-598X
Type :
jour
DOI :
10.1109/LGRS.2014.2354399
Filename :
6905758
Link To Document :
بازگشت