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Author (up) Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud edit   pdf
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  Title Cycle Generative Adversarial Network: Towards A Low-Cost Vegetation Index Estimation Type Conference Article
  Year 2021 Publication 28th IEEE International Conference on Image Processing Abbreviated Journal  
  Volume Issue Pages 19-22  
  Keywords  
  Abstract This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI). The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach.  
  Address Anchorage-Alaska; USA; September 2021  
  Corporate Author Thesis  
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  Area Expedition Conference ICIP  
  Notes MSIAU; 600.130; 600.122; 601.349 Approved no  
  Call Number Admin @ si @ SSV2021b Serial 3579  
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