Prof. Ireneusz Ewiak Profile

Prof. Ireneusz Ewiak

at Wojskowa Akademia Techniczna

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Publications (2)

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Proceedings Article | 9 October 2018 Paper

Improvement of interpretability of archival aerial photographs using remote sensing tools

Ireneusz Ewiak, Katarzyna Siok, Anna Schismak, Agnieszka Jenerowicz

Proceedings Volume 10789, 1078925 (2018) https://doi.org/10.1117/12.2325813

KEYWORDS: Photography, Image processing, Image segmentation, Visualization, Remote sensing, Visual analytics, Image quality, Image classification, Buildings, Algorithm development

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Archival aerial photographs are a valuable source of information on the land cover. Unfortunately, these are singlechannel, monochromatic data, which means that the interpretation possibilities of these data are significantly limited. Therefore, a research was conducted in order to increase the possibility of detecting and identifying photographed elements of both natural and anthropogenic land cover. In this paper, a semi-automatic method of coloring archival photographs that uses image processing tools used in popular remote sensing software is presented. It is based on the segmentation, classification, pseudocoloring and pansharpening process. The tests were carried out on a set of aerial photos acquired in the 1950s, where mainly agricultural and forest areas with single rural buildings were photographed. Evaluation of the developed method was done through a visual analysis of the generated color images. The visual assessment was supplemented with a calculation of the value of the color accuracy index for each land cover class tested, i.e., forests, low vegetation, bare soils, water and anthropogenic objects. The presented method gives the opportunity to increase the visual quality of aerial images by giving them colors similar to natural ones while maintaining the level of detail. The visual enhancement of archival images, on the other hand, enables the automation of the identification process and analysis of photographed objects that have so far been performed manually only based on the interpreter's experience.

Proceedings Article | 9 October 2018 Paper

Enhancement of spectral quality of natural land cover in the pan-sharpening process

Katarzyna Siok, Ireneusz Ewiak, Agnieszka Jenerowicz

Proceedings Volume 10789, 107891P (2018) https://doi.org/10.1117/12.2325663

KEYWORDS: Earth observing sensors, Image fusion, Near infrared, Vegetation, Image processing, Satellites, Landsat, Reflectivity, Satellite imaging

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A high spectral resolution of remote sensing images is crucial for the precise environmental research purpose. Therefore, it is essential to preserve the high spectral resolution of satellite imagery in the pan-sharpening process. Unfortunately, the use of original panchromatic (PAN) images as high-spatial data in the pan-sharpening process is ambiguous with maintaining the spectral information about photographed objects as high as possible. Therefore, there is a reasonable need to simulate the high-spatial band. The research has involved the integration of two satellite multispectral (MS) images: Landsat-8 imagery (30 m) used as high-spectral data and Sentinel-2 imagery (10 m) used as high-spatial data. The new panchromatic bands based on Sentinel-2 data was performed. The channel combinations used in spectral indices such as: Difference Vegetation Index (DVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Simple Ratio (SR), and Ratio Vegetation Index (RVI) were utilized for the simulation purpose. Either the spectral quality of sharpened images and their suitability to a classification process were verified. In the assessment stage, a visual analysis, spectral quality indices, a comparison of spectral reflectance characteristics of natural land cover, and a supervised classification were applied. The research indicates the necessity to simulate the high-spatial channel to pan-sharpening process depending on the spectral resolution of the high-spectral imagery as well as depending on the type of object of interest (OOI). The application of appropriate modifications of the original high-spatial images makes it possible to keep the spectral information about photographed objects at a higher level than using original data. Thus, it is essential for the object identification process, change detection, and land management.

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