In this paper, multivariate regression is adopted to improve spectral quality, without diminishing spatial quality, in image fusion methods based on the well-established component substitution (CS) approach. A general scheme that is capable of modeling any CS image fusion method is presented and discussed. According to this scheme, a generalized intensity component is defined as the weighted average of the multispectral (MS) bands. The weights are obtained as regression coefficients between the MS bands and the spatially degraded panchromatic (Pan) image, with the aim of capturing the spectral responses of the sensors. Once it has been integrated into the Gram-Schmidt spectral-sharpening method, which is implemented in environment for visualizing images (ENVI) program, and into the generalized intensity-hue-saturation fusion method, the proposed preprocessing module allows the production of fused images of the same spatial sharpness but of increased spectral quality with respect to the standard implementations. In addition, quantitative scores carried out on spatially degraded data clearly confirm the superiority of the enhanced methods over their baselines.

This paper compares two general and formal solutions to the problem of fusion of multispectral images with high-resolution panchromatic observations. The former exploits the undecimated discrete wavelet transform, which is an octave bandpass representation achieved from a conventional discrete wavelet transform by omitting all decimators and upsampling the wavelet filter bank. The latter relies on the generalized Laplacian pyramid, which is another oversampled structure obtained by recursively subtracting from an image an expanded decimated lowpass version. Both the methods selectively perform spatial-frequencies spectrum substitution from an image to another. In both schemes, context dependency is exploited by thresholding the local correlation coefficient between the images to be merged, to avoid injection of spatial details that are not likely to occur in the target image. Unlike other multiscale fusion schemes, both the present decompositions are not critically subsampled, thus avoiding possible impairments in the fused images, due to missing cancellation of aliasing terms. Results are presented and discussed on SPOT data.

This paper aims at defining a new paradigm (hypersharpening) in remote sensing image fusion. In fact, due to the development of new instruments, thinking only in terms of pansharpening is reductive. Even though some expressions as hyperspectral (HS) pansharpening already exist, there is not a suitable definition when multispectral/hyperspectral data are used as source to extract spatial details. After defining the hypersharpening framework, we draw the readers' attention to its peculiar characteristics, by proposing and evaluating two hypersharpening methods. Experiments are carried out on the data produced by the updated version of SIM-GA imager, designed by Selex ES, which is composed by a panchromatic camera and two spectrometers in the VNIR and SWIR spectral ranges, respectively. Owing to the different resolution factors among panchromatic, VNIR and SWIR data sets, we can apply hypersharpening to fuse SWIR data to VNIR resolution. Comparisons of hypersharpening with “traditional” pansharpening show hypersharpening is more effective.