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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | Image Vegetation Index through a Cycle Generative Adversarial Network | Type | Conference Article | ||
| Year | 2019 | Publication | Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States | Abbreviated Journal | |
| Volume | Issue | Pages  |
1014-1021 | ||
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| Abstract | This paper proposes a novel approach to estimate the Normalized Difference Vegetation Index (NDVI) just from an RGB image. The NDVI values are obtained by using images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The cycled GAN network is able to obtain a NIR image from a given gray scale image. It is trained by using unpaired set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are obtained from the provided RGB images). Then, the NIR image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous approaches are also provided. |
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| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 106 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | Near InfraRed Imagery Colorization | Type | Conference Article | ||
| Year | 2018 | Publication | 25 th IEEE International Conference on Image Processing, ICIP 2018 | Abbreviated Journal | |
| Volume | Issue | Pages |
2237-2241 | ||
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| Abstract | This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multiple loss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of the art methods using standard metrics.1 Index Terms—Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Infrared Imagery colorization. |
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| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 81 | ||
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| Author | Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla | ||||
| Title | Cycle generative adversarial network: towards a low-cost vegetation index estimation | Type | Conference Article | ||
| Year | 2021 | Publication | IEEE International Conference on Image Processing (ICIP 2021) | Abbreviated Journal | |
| Volume | 2021-September | Issue | Pages |
2783-2787 | |
| Keywords | CyclicGAN, NDVI, near infrared spectra, instance normalization. | ||||
| 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. | ||||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 164 | ||
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| Author | Ricaurte P; Chilán C; Cristhian A. Aguilera; Boris X. Vintimilla; Angel D. Sappa | ||||
| Title | Feature Point Descriptors: Infrared and Visible Spectra | Type | Journal Article | ||
| Year | 2014 | Publication | Sensors Journal | Abbreviated Journal | |
| Volume | Vol. 14 | Issue | Pages |
pp. 3690-3701 | |
| Keywords | cross-spectral imaging; feature point descriptors | ||||
| Abstract | This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given. | ||||
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| Language | English | Summary Language | English | Original Title | |
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| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 28 | ||
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