Generating synthetic multispectral images using neural style transfer: A study with application in channel alignment

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We can obtain information about food plantations by imaging technology such as satellite and unmanned aerial vehicles which enables rapid decision-making. Unmanned aerial vehicles provide fast and precise high-resolution multispectral imaging of plantation areas. However, unmanned aerial vehicle images are subject to some misalignment among their bands mainly due to the sensor’s displacement, vehicle movements, and others. To quantitatively evaluate channel alignment algorithms, it is necessary to have fully aligned images. For this purpose, we propose a method for generating fully aligned synthetic multispectral images based on real misaligned unmanned aerial vehicle multispectral images using Neural Style Transferring algorithms. We applied our methods over a real multispectral images dataset and we generated a new synthetic aligned image for each original misaligned multispectral image. Our experiments demonstrate that images generated by our method have fully aligned channels, and the new channels resemble the original ones. We also designed two case studies that demonstrate the applicability of our approach.