Naslov
Independent Component Analysis for Hyperspectral Remote Sensing

Autori
Du, Quian ; Kopriva, Ivica ; Szu, Harold

Izvornik
Optical Engineering (0091-3286) 45 (2006), 1; 017008:1-13

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
independent-component analysis; principal-component analysis

Sažetak
We investigate the application of independent-component analysis (ICA) to remotely sensed hyperspectral image classification. We focus on the performance of two well-known and frequently used ICA algorithms: joint approximate diagonalization of eigenmatrices (JADE) and FastICA ; but the proposed method is applicable to other ICA algorithms. The major advantage of using ICA is its ability to classify objects with unknown spectral signatures in an unknown image scene, i.e., unsupervised classification. However, ICA suffers from computational expensiveness, which limits its application to high-dimensional data analysis. In order to make it applicable or reduce the computation time in hyperspectral image classification, a data-preprocessing procedure is employed to reduce the data dimensionality. Instead of using principal-component analysis (PCA), a noise-adjusted principal-components (NAPC) transform is employed for this purpose, which can reorganize the original data with respect to the signal-to-noise ratio, a more appropriate image-ranking criterion than variance in PCA. The experimental results demonstrate that the major principal components from the NAPC transform can better maintain the object information in the original data than those from PCA. As a result, an ICA algorithm can provide better object classification

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika

POVEZANOST RADA