Independent-component analysis for hyperspectral remote sensing imagery classification

Harold Szu, FELLOW SPIE Office of Naval Research Arlington, Virginia 22217 Abstract. 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 principalcomponent 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. © 2006 Society of Photo-Optical Instrumentation Engineers. DOI: 10.1117/1.2151172