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Mulham91 / Multi-Spectral-Image-Synthesis-for-Crop-Weed-Segmentation-in-Precision-Farming

In this work, we propose an alternative solution with respect to the common data augmentation techniques, applying it to the fundamental problem of crop/weed segmentation in precision farming. Starting from real images, we create semi-artificial samples by replacing the most relevant object classes (i.e., crop and weeds) with synthesized counterparts. To do that, we employ a conditional GAN (cGAN), where the generative model is trained by conditioning the shape of the generated object. Moreover, in addition to RGB data, we take into account also near-infrared information, generating four channel multi-spectral synthetic images.
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How to generate four channel multi-spectral synthetic images. #1

Open MuhammadAsadJaved opened 4 years ago

MuhammadAsadJaved commented 4 years ago

Hi, May I ask how you generate multi-spectral synthetic images? Is it using GAN or just using existing RGB and NIR images?

Mulham91 commented 3 years ago

Hi, We used existing RGB and NIR images for training GAN to generate multi-spectral synthetic images.