Hierarchical PCA Techniques for Fusing Spatial and Spectral Observations With Application to MISR and Monitoring Dust Storms

• Published in: IEEE geoscience and remote sensing letters 2007-10, Vol.4 (4), p.678-682
• Main Authors: Agarwal, A., El-Askary, H.M., El-Ghazawi, T., Kafatos, M., Le-Moigne, J.
• Format: Article
• Language: English
• Subjects: Cameras; Classification; Covariance matrix; Data fusion; dust storms; Earth; Eigenvalues and eigenfunctions; Monitoring; Multiangle Imaging SpectroRadiometer (MISR); Personal communication networks; Principal component analysis; principal component analysis (PCA); Principal components analysis; Redundancy; Remote sensing; Spectra; Spectroradiometers; Storms; Studies; Vices
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2007.904467

In this letter, we propose hierarchical principal component analysis (HPCA) techniques for fusing spatial and spectral data, and compare them to direct principal component analysis (DPCA) over Multiangle Imaging SpectroRadiometer (MISR) data. It is shown that the proposed methods are significantly faster than DPCA. In case of DPCA, we merge the 20 different images resulting from the four spectral bands over the nadir and the four forward angles. In the hierarchical case, we first merge the information from the four spectral camera bands; then, we integrate the spatial information from the five cameras in the second step (or vice versa ) by applying principal component analysis (PCA) twice. The classification results show that fused data using HPCA compare favorably to DPCA or to classification using the original data. This is because applying PCA to one particular data domain (e.g., spectral data followed by spatial data or vice versa ) tends to better remove redundancies and enhance features within that domain. In addition, classification through hierarchical data fusion results in computational savings over the other methods.