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Landslide inventory maps: New tools for an old problem

Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community.

A Critical Comparison Among Pansharpening Algorithms

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.

Improving Component Substitution Pansharpening Through Multivariate Regression of MS $+$ Pan Data

Pixel-level image fusion

This work presents a multiresolution framework for merging a multispectral image having an arbitrary number of bands with a higher-resolution panchromatic observation. The fusion method relies on the generalized Laplacian pyramid (GLP), which is a multiscale, oversampled structure. The goal is to selectively perform injection of spatial frequencies from an image to another with the constraint of thoroughly retaining the spectral information of the coarser data. The novel idea is that a model of the modulation transfer functions (MTF) of the multispectral scanner is exploited to design the GLP reduction filter. Thus, the interband structure model (IBSM), which is calculated at the coarser scale, where both MS and PAN data are available, can be extended to the finer scale, without the drawback of the poor enhancement occurring when MTFs are assumed to be ideal filters. Experiments carried out on QuickBird data demonstrate that a superior spatial enhancement, besides the spectral quality typical of injection methods, is achieved by means of the MTF-adjusted fusion.

MTF-tailored Multiscale Fusion of High-resolution MS and Pan Imagery

The spatial resolution of a multispectral digital image is enhanced in a process of the type wherein a higher spatial resolution panchromatic image is merged with a plurality of lower spatial resolution spectral band images. A lower spatial resolution panchromatic image is simulated and a Gram-Schmidt transformation is performed on the simulated lower spatial resolution panchromatic image and the plurality of lower spatial resolution spectral band images. The simulated lower spatial resolution panchromatic image is employed as the first band in the Gram-Schmidt transformation. The statistics of the higher spatial resolution panchromatic image are adjusted to match the statistics of the first transform band resulting from the Gram-Schmidt transformation and the higher spatial resolution panchromatic image (with adjusted statistics) is substituted for the first transform band resulting from the Gram-Schmidt transformation to produce a new set of transform bands. Finally, the inverse Gram-Schmidt transformation is performed on the new set of transform bands to produce the enhanced spatial resolution multispectral digital image.

Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening

A method of removing columnar streaks from a digital image of the type in which it is assumed that pixels in a predetermined region near a given pixel are strongly related to each other and employing gain and offset values to compute streak removal information, a test is performed for a strong relation between the pixels in a predetermined region near a given pixel and streak removal information is computed only if such a strong relationship exists, whereby image content that does not extend the full length of the image in the column direction will not be interpreted as a streak.

Craig A. Laben

Papers

Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening

Adaptive process for removing streaks in digital images