Abstract
In precision agriculture, remote sensing using unmanned aerial vehicles (UAVs) can well complement and, in several cases, even completely replace satellite imagery. However, it is important to harmonize their signals for consistent use. In this work, the problem of spectral harmonization is considered. We propose two new methods of spectral harmonization: method of root-polynomial correction (RPC) and model-based spectral harmonization (MBSH). The methods are evaluated on a synthetic dataset that is generated using AVIRIS hyperspectral data and known spectral sensitivities of two sensors: Sentinel-2A (satellite) and Parrot Sequoia+ (UAV). The RPC has outperformed all state-of-the-art methods in most bands. The MBSH method, despite of moderate result, has an important advantage: it does not require retraining for sensors with different spectral sensitivities.
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This work was supported by the Russian Science Foundation, project no. 20-61-47089.
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Translated by A. Chikishev
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Nurmukhametov, A.L., Sidorchuk, D.S., Konovalenko, I.A. et al. Spectral Harmonization of UAV and Satellite Data for the Needs of Precision Agriculture. J. Commun. Technol. Electron. 67 (Suppl 2), S275–S282 (2022). https://doi.org/10.1134/S1064226922140054
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DOI: https://doi.org/10.1134/S1064226922140054