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Author (up) Patricia L. Suárez, Angel D. Sappa and Boris X. Vintimilla
Title Deep learning-based vegetation index estimation Type Book Chapter
Year 2021 Publication Generative Adversarial Networks for Image-to-Image Translation Book. Abbreviated Journal
Volume Chapter 9 Issue Issue 2 Pages 205-232
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Call Number cidis @ cidis @ Serial 137
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Author (up) 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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Call Number cidis @ cidis @ Serial 164
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Author (up) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture Type Conference Article
Year 2017 Publication 19th International Conference on Image Analysis and Processing. Abbreviated Journal
Volume Issue Pages 287-297
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Call Number gtsi @ user @ Serial 66
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Author (up) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Learning Image Vegetation Index through a Conditional Generative Adversarial Network Type Conference Article
Year 2017 Publication 2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM) Abbreviated Journal
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Call Number gtsi @ user @ Serial 70
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Author (up) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Vegetation Index Estimation from Monospectral Images Type Conference Article
Year 2018 Publication 15th International Conference, Image Analysis and Recognition (ICIAR 2018), Póvoa de Varzim, Portugal. Lecture Notes in Computer Science Abbreviated Journal
Volume 10882 Issue Pages 353-362
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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.
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Call Number gtsi @ user @ Serial 82
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Author (up) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images Type Conference Article
Year 2018 Publication International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing Abbreviated Journal
Volume 721 Issue Pages
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Abstract This paper proposes a novel approach to use cross-spectral

images to achieve a better performance with the proposed Adaptive Harris

corner detector comparing its obtained results with those achieved

with images of the visible spectra. The images of urban, field, old-building

and country category were used for the experiments, given the variety of

the textures present in these images, with which the complexity of the

proposal is much more challenging for its verification. It is a new scope,

which means improving the detection of characteristic points using crossspectral

images (NIR, G, B) and applying pruning techniques, the combination

of channels for this fusion is the one that generates the largest

variance based on the intensity of the merged pixels, therefore, it is that

which maximizes the entropy in the resulting Cross-spectral images.

Harris is one of the most widely used corner detection algorithm, so

any improvement in its efficiency is an important contribution in the

field of computer vision. The experiments conclude that the inclusion of

a (NIR) channel in the image as a result of the combination of the spectra,

greatly improves the corner detection due to better entropy of the

resulting image after the fusion, Therefore the fusion process applied to

the images improves the results obtained in subsequent processes such as

identification of objects or patterns, classification and/or segmentation.
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Notes 1 Approved no
Call Number gtsi @ user @ Serial 84
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Author (up) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Cross-spectral image dehaze through a dense stacked conditional GAN based approach. Type Conference Article
Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) Abbreviated Journal
Volume Issue Pages 358-364
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Abstract This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results.
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Call Number gtsi @ user @ Serial 92
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Author (up) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Cross-spectral Image Patch Similarity using Convolutional Neural Network Type Conference Article
Year 2017 Publication 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) Abbreviated Journal
Volume Issue Pages 1-5
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Notes Approved no
Call Number cidis @ cidis @ Serial 57
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Author (up) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Learning to Colorize Infrared Images Type Conference Article
Year 2017 Publication 15th International Conference on Practical Applications of Agents and Multi-Agent Systems Abbreviated Journal
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Notes Approved no
Call Number cidis @ cidis @ Serial 58
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Author (up) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Infrared Image Colorization based on a Triplet DCGAN Architecture. Type Conference Article
Year 2017 Publication 13th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2017. (This paper has been selected as “Best Paper Award” ) Abbreviated Journal
Volume 2017-July Issue Pages 212-217
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Call Number cidis @ cidis @ Serial 62
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