Estimation of the Number of Decomposition Levels for a Wavelet-Based Multiresolution Multisensor Image Fusion

• Published in: IEEE transactions on geoscience and remote sensing Vol. 44; no. 12; pp. 3674 - 3686
• Main Authors: Pradhan, P.S., King, R.L., Younan, N.H., Holcomb, D.W.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2006; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied geophysics; Computation; Decomposition; Earth sciences; Earth, ocean, space; Exact sciences and technology; Image analysis; Image fusion; Image resolution; Imagery; Internal geophysics; Mathematical analysis; multiresolution; multispectral (MS); pan-sharpening; Principal component analysis; Roads; shift invariant; Similarity; Spatial resolution; Spectra; Stress; Studies; Transportation; Vegetation mapping; wavelet transform; Wavelet transforms; algorithms; pan-sharpening; multispectral remote sensing; Space remote sensing; imagery; multispectral (MS); wavelet transform; multiresolution; classification; shift invariant; Image fusion; quality
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2006.881758

The wavelet-based scheme for the fusion of multispectral (MS) and panchromatic (PAN) imagery has become quite popular due to its ability to preserve the spectral fidelity of the MS imagery while improving its spatial quality. This is important if the resultant imagery is used for automatic classification. Wavelet-based fusion results depend on the number of decomposition levels applied in the wavelet transform. Too few decomposition levels result in poor spatial quality fused images. On the other hand, too many levels reduce the spectral similarity between the original MS and the pan-sharpened images. If the shift-invariant wavelet transform is applied, each excessive decomposition level results in a large computational penalty. Thus, the choice of the number of decomposition levels is significant. In this paper, PAN and MS image pairs with different resolution ratios were fused using the shift-invariant wavelet transform, and the optimal decomposition levels were determined for each resolution ratio. In general, it can be said that the fusion of images with larger resolution ratios requires a higher number of decomposition levels. This paper provides the practitioner an understanding of the tradeoffs associated with the computational demand and the spatial and spectral quality of the wavelet-based fusion algorithm as a function of the number of decomposition levels