Abstract
The standard ratio of spatial resolution between bands for high resolution satellites is 1:4, which is typical when combining images obtained from the same sensor. However, the cost of simultaneously purchasing a set of panchromatic and multispectral images is still relatively high. There is therefore a need to develop methods of data fusion of very high resolution panchromatic imagery with low-cost multispectral data (e.g., Landsat). Combining high resolution images with low resolution images broadens the scope of use of satellite data, however, it is also accompanied by the problem of a large ratio between spatial resolutions, which results in large spectral distortions in the merged images. The authors propose a modification of the panchromatic image in such a way that it includes the spectral and spatial information from both the panchromatic and multispectral images to improve the quality of spectral data integration. This fusion is done based on a weighted average. The weight is determined using a coefficient, which determines the ratio of the amount of information contained in the corresponding pixels of the integrated images. The effectiveness of the author’s algorithm had been tested for six of the most popular fusion methods. The proposed methodology is ideal mainly for statistical and numerical methods, especially Principal Component Analysis and Gram-Schmidt. The author’s algorithm makes it possible to lower the root mean square error by up to 20% for the Principal Component Analysis. The spectral quality was also increased, especially for the spectral bands extending beyond the panchromatic image, where the correlation rose by 18% for the Gram-Schmidt orthogonalization.
Highlights
In recent years, there had been significant developments in satellite techniques
Multispectral images are usually characterized by a spatial resolution about four times lower than the resolution panchromatic imagery from the same sensor
The worst results were reached when using the IHS, Principal Component Analysis (PCA) and Brovey transform methods, and the High Pass Filter (HPF) method gave the best results, with the correlation coefficient between the fused imagery and the original multispectral image ranging from 0.25 to 0.75 depending on the method and area [14]. These results indicate a much lower spectral quality of the sharpened images when the resolution ratio is high, in this article, we try to solve the problem presented by proposing a new algorithm
Summary
There had been significant developments in satellite techniques. There is already easy access to data from very high resolution satellites (VHRS) [1] that record imagery with resolutions reaching even around 30 cm, for example WorldView-3 in its panchromatic channel. To integrate high spatial resolution data with a high spectral resolution, pansharpening algorithms are used [2]. They are used both for satellite and aerial data [3,4,5,6] and for data obtained from low altitudes [7,8]. The results of such image fusion are often used in urban and environmental analyses [9,10]
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