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Remote Sens. 2017, 9(6), 639; doi:10.3390/rs9060639

A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion

Department of Remote Sensing, Photogrammetry and Imagery Intelligence, Institute of Geodesy, Faculty of Civil Engineering and Geodesy, Military University of Technology, 01-476 Warszawa, Poland
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Academic Editors: Xiaofeng Li and Prasad S. Thenkabail
Received: 10 May 2017 / Revised: 9 June 2017 / Accepted: 16 June 2017 / Published: 21 June 2017
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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. View Full-Text
Keywords: data fusion; pansharpening; spectral quality; spatial resolution; Principal Component Analysis; Gram-Schmidt; intensity data fusion; pansharpening; spectral quality; spatial resolution; Principal Component Analysis; Gram-Schmidt; intensity
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Grochala, A.; Kedzierski, M. A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion. Remote Sens. 2017, 9, 639.

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