Abstract
This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance details, resulting in a sharp NDVI image. Then, the discriminative model estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Rouse Jr., J., Haas, R., Schell, J., Deering, D.: Monitoring vegetation systems in the great plains with erts (1974)
Ricaurte, P., Chilán, C., Aguilera-Carrasco, C.A., Vintimilla, B.X., Sappa, A.D.: Feature point descriptors: infrared and visible spectra. Sensors 14, 3690–3701 (2014)
Aguilera, C.A., Aguilera, F.J., Sappa, A.D., Aguilera, C., Toledo, R.: Learning cross-spectral similarity measures with deep convolutional neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, p. 9. IEEE (2016)
Suárez, P.L., Sappa, A.D., Vintimilla, B.X.: Cross-spectral image patch similarity using convolutional neural network. In: 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), pp. 1–5. IEEE (2017)
Suárez, P.L., Sappa, A.D., Vintimilla, B.X.: Learning image vegetation index through a conditional generative adversarial network. In: 2nd Ecuador Technical Chapters Meeting (2017)
Suárez, P.L., Sappa, A.D., Vintimilla, B.X.: Learning to colorize infrared images. In: De la Prieta, F., Vale, Z., Antunes, L., Pinto, T., Campbell, A.T., Julián, V., Neves, A.J.R., Moreno, M.N. (eds.) PAAMS 2017. AISC, vol. 619, pp. 164–172. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61578-3_16
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Suárez, P.L., Sappa, A.D., Vintimilla, B.X.: Infrared image colorization based on a triplet DCGAN architecture. In: Computer Vision and Pattern Recognition (2017)
Brown, M., Süsstrunk, S.: Multi-spectral SIFT for scene category recognition. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 177–184. IEEE (2011)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)
Acknowledgment
This work has been partially supported by: the ESPOL project PRAIM (FIEC-09-2015); the Spanish Government under Projects TIN2014-56919-C3-2-R and TIN2017-89723-P; and the “CERCA Programme/Generalitat de Catalunya”. The authors would like to thank NVIDIA for GPU donations.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Suárez, P.L., Sappa, A.D., Vintimilla, B.X. (2018). Vegetation Index Estimation from Monospectral Images. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-93000-8_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92999-6
Online ISBN: 978-3-319-93000-8
eBook Packages: Computer ScienceComputer Science (R0)