Title :
Dimensionality Reduction of Hyperspectral Data Based on ISOMAP Algorithm
Author :
Guangjun, Dong ; Yongsheng, Zhang ; Song, Ji
Abstract :
In this paper, a new manifold learning method to reduce the dimension of hyperspectral data is proposed. In this method, ISOMAP algorithm is used to extract the inherent manifold of hyperspectral data to transform the high-dimensional space into a low-dimensional space. Experiments show that the method is effective, meaningful, and provides a new way for reducing the dimension of hyperspectral data while expands the application area of manifold learning in the hyperspectral data processing filed.
Keywords :
data reduction; learning (artificial intelligence); ISOMAP algorithm; dimensionality reduction; high-dimensional space; hyperspectral data processing; low-dimensional space; manifold learning; Data engineering; Data mining; Data processing; Hyperspectral imaging; Hyperspectral sensors; Instruments; Joining processes; Learning systems; Manifolds; Remote sensing; Dimensionality Reduction; Manifold Learning;
Conference_Titel :
Electronic Measurement and Instruments, 2007. ICEMI '07. 8th International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4244-1136-8
Electronic_ISBN :
978-1-4244-1136-8
DOI :
10.1109/ICEMI.2007.4351072
