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Proceedings Paper

The fusion of satellite and UAV data: simulation of high spatial resolution band
Author(s): Agnieszka Jenerowicz; Katarzyna Siok; Malgorzata Woroszkiewicz; Agata Orych
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Paper Abstract

Remote sensing techniques used in the precision agriculture and farming that apply imagery data obtained with sensors mounted on UAV platforms became more popular in the last few years due to the availability of low- cost UAV platforms and low- cost sensors. Data obtained from low altitudes with low- cost sensors can be characterised by high spatial and radiometric resolution but quite low spectral resolution, therefore the application of imagery data obtained with such technology is quite limited and can be used only for the basic land cover classification. To enrich the spectral resolution of imagery data acquired with low- cost sensors from low altitudes, the authors proposed the fusion of RGB data obtained with UAV platform with multispectral satellite imagery. The fusion is based on the pansharpening process, that aims to integrate the spatial details of the high-resolution panchromatic image with the spectral information of lower resolution multispectral or hyperspectral imagery to obtain multispectral or hyperspectral images with high spatial resolution. The key of pansharpening is to properly estimate the missing spatial details of multispectral images while preserving their spectral properties. In the research, the authors presented the fusion of RGB images (with high spatial resolution) obtained with sensors mounted on low- cost UAV platforms and multispectral satellite imagery with satellite sensors, i.e. Landsat 8 OLI. To perform the fusion of UAV data with satellite imagery, the simulation of the panchromatic bands from RGB data based on the spectral channels linear combination, was conducted. Next, for simulated bands and multispectral satellite images, the Gram-Schmidt pansharpening method was applied. As a result of the fusion, the authors obtained several multispectral images with very high spatial resolution and then analysed the spatial and spectral accuracies of processed images.

Paper Details

Date Published: 2 November 2017
PDF: 12 pages
Proc. SPIE 10421, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX, 104211Z (2 November 2017);
Show Author Affiliations
Agnieszka Jenerowicz, MUT Military Univ. of Technology (Poland)
Katarzyna Siok, MUT Military Univ. of Technology (Poland)
Malgorzata Woroszkiewicz, MUT Military Univ. of Technology (Poland)
Agata Orych, MUT Military Univ. of Technology (Poland)

Published in SPIE Proceedings Vol. 10421:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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