6 July 2017 Enhancement of spectral quality of archival aerial photographs using satellite imagery for detection of land cover
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Abstract
Archival aerial photographs are often the only reliable source of information about the area. However, these data are single-band data that do not allow unambiguous detection of particular forms of land cover. Thus, the authors of this article seek to develop a method of coloring panchromatic aerial photographs, which enable increasing the spectral information of such images. The study used data integration algorithms based on pansharpening, implemented in commonly used remote sensing programs: ERDAS, ENVI, and PCI. Aerial photos and Landsat multispectral data recorded in 1987 and 2016 were chosen. This study proposes the use of modified intensity-hue-saturation and Brovey methods. The use of these methods enabled the addition of red-green-blue (RGB) components to monochrome images, thus enhancing their interpretability and spectral quality. The limitations of the proposed method relate to the availability of RGB satellite imagery, the accuracy of mutual orientation of the aerial and the satellite data, and the imperfection of archival aerial photographs. Therefore, it should be expected that the results of coloring will not be perfect compared to the results of the fusion of recent data with a similar ground sampling resolution, but still, they will allow a more accurate and efficient classification of land cover registered on archival aerial photographs.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Katarzyna Siok, Katarzyna Siok, Agnieszka Jenerowicz, Agnieszka Jenerowicz, Małgorzata Woroszkiewicz, Małgorzata Woroszkiewicz, } "Enhancement of spectral quality of archival aerial photographs using satellite imagery for detection of land cover," Journal of Applied Remote Sensing 11(3), 036001 (6 July 2017). https://doi.org/10.1117/1.JRS.11.036001 . Submission: Received: 10 April 2017; Accepted: 12 June 2017
Received: 10 April 2017; Accepted: 12 June 2017; Published: 6 July 2017
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