Wavelet and pyramid techniques for multisensor data fusion: a performance comparison varying with scale ratios

Goal of this paper is to provide a quantitative performance evaluation of multiresolution schemes capable to carry out feature-based fusion of data collected by multispectral and panchromatic imaging sensors having different spectral and ground resolutions. To this aim a set of quantitative parameters has been recently proposed. Both visual quality, regarded as contrast, presence of fine details, and absence of impairments and artifacts (e.g., blur, ringing), and spectral fidelity (i.e., preservation of spectral signatures) are concerned and embodied in the measurements. Out of the three methods compared, respectively based on highpass filtering (HPF), wavelet transform (WT), and generalized Laplacian pyramid (GLP), the latter two are far more efficient than the former, thus establishing the advantages for data fusion of a formally multiresolution analysis.