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Your search keyword '"Fernandez-Beltran, Ruben"' showing total 146 results
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146 results on '"Fernandez-Beltran, Ruben"'

1. Semi-supervised Classification for Remote Sensing Datasets

Author

Hernandez-Sequeira, Itza, Fernandez-Beltran, Ruben, Xu, Yonghao, Ghamisi, Pedram, Pla, Filiberto, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Foresti, Gian Luca, editor, Fusiello, Andrea, editor, and Hancock, Edwin, editor

Published
2023
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12. Latent Topic Encoding for Content-Based Retrieval

Author

Fernandez-Beltran, Ruben, Pla, Filiberto, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Paredes, Roberto, editor, Cardoso, Jaime S., editor, and Pardo, Xosé M., editor

Published
2015
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21. An Interactive Video Retrieval Approach Based on Latent Topics

Author

Fernández-Beltran, Rubén, Pla, Filiberto, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Petrosino, Alfredo, editor

Published
2013
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22. Vocabulary Reduction in BoW Representing by Topic Modeling

Author

Fernández-Beltran, Rubén, Montoliu, Raul, Pla, Filiberto, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sanches, João M., editor, Micó, Luisa, editor, and Cardoso, Jaime S., editor

Published
2013
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23. SEN23E: A Cloudless Geo-Referenced Multi-Spectral Sentinel-2/Sentinel-3 Dataset for Data Fusion Analysis

Author

Ibáñez Fernández, Damián, Fernandez-Beltran, Ruben, and Pla, Filiberto

Subjects
data fusion, remote sensing, multi-spectral, sentinel-3 (S3), dataset, sentinel-2 (S2)
Abstract

Ponència presentada en el IEEE International Symposium on Geoscience and Remote Sensing (IGARSS 2022) The availability of geo-referenced coupled data of dif-ferent platforms is essential to train remote sensing (RS) multi-modal classification and bio-phyiscal parameter esti-mation learning methods. To properly develop a general-izing model different scenes and topographies are required. For this purpose, different multi-modal datasets have been published for the last years. Nevertheless, to our knowl-edge there is not any dataset composed of Sentinel-2 (S2) and Sentinel-3 (S3) geo-referenced images. In this paper we present SEN23, a dataset composed of 100 complete multi-spectral S2 and S3 paired images of different locations along Europe from the 2021 summer. The coupled images were obtained with a time difference of three or less days, containing less than a 1 % of cloud coverage and have a resolution difference of × 15. SEN23E is expected to help with the development of new multi-spectral, multi-resolution and multi-modal models for complex tasks which need con-text and complete images. SEN23E will be available at https://github.com/ibanezdf/SEN23E.

Published
2022

25. Attention Mechanisms in Convolutional Neural Networks for Nitrogen Treatment Detection in Tomato Leaves Using Hyperspectral Images.

Author

Benmouna, Brahim, Pourdarbani, Raziyeh, Sabzi, Sajad, Fernandez-Beltran, Ruben, García-Mateos, Ginés, and Molina-Martínez, José Miguel

Subjects
CONVOLUTIONAL neural networks, TOMATOES, NITROGEN fertilizers, OPTICAL spectroscopy, NEAR infrared spectroscopy, SPECTRAL reflectance
Abstract

Nitrogen is an essential macronutrient for the growth and development of tomatoes. However, excess nitrogen fertilization can affect the quality of tomato fruit, making it unattractive to consumers. Consequently, the aim of this study is to develop a method for the early detection of excessive nitrogen fertilizer use in Royal tomato by visible and near-infrared spectroscopy. Spectral reflectance values of tomato leaves were captured at wavelengths between 400 and 1100 nm, collected from several treatments after application of normal nitrogen and on the first, second, and third days after application of excess nitrogen. A new method based on convolutional neural networks (CNN) with an attention mechanism was proposed to perform the estimation of nitrogen overdose in tomato leaves. To verify the effectiveness of this method, the proposed attention mechanism-based CNN classifier was compared with an alternative CNN having the same architecture without integrating the attention mechanism, and with other CNN models, AlexNet and VGGNet. Experimental results showed that the CNN with an attention mechanism outperformed the alternative CNN, achieving a correct classification rate (CCR) of 97.33% for the treatment, compared with a CCR of 94.94% for the CNN alone. These findings will help in the development of a new tool for rapid and accurate detection of nitrogen fertilizer overuse in large areas. [ABSTRACT FROM AUTHOR]

Published
2023
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41. IEEE

Author

Fernandez-Beltran, Ruben, Ibáñez Fernández, Damián, kang, jian, and Pla, Filiberto

Subjects
vegetation indices (VI), satellites, MODIS, inter-sensor regression, sentinel-3 (S3), vegetation mapping, instruments, flexible printed circuits, sentinel-2 (S2), earth, spatial resolution, temporal resolution
Abstract

Sentinel missions provide widespread opportunities of exploiting inter-sensor synergies to improve the operational monitoring of terrestrial photosynthetic activity and canopy structural variations using vegetation indices (VI). In this context, continuous and consistent temporal data are logically required to rapidly detect vegetation changes across sensors. Nonetheless, the existing temporal limitations inherent to satellite orbits, cloud occlusions, data degradation, and many other factors may severely constrain the availability of data involving multiple satellites. In response, this letter proposes a novel deep 3-D convolutional regression network (3CRN) for temporally enhancing Sentinel-3 (S3) VI by taking advantage of inter-sensor Sentinel-2 (S2) observations. Unlike existing regression and deep learning-based methods, the proposed approach allows convolutional kernels to slide across the temporal dimension to exploit not only the higher spatial resolution of the S2 instrument but also its own temporal evolution to better estimate time-resolved VI in S3. To validate the proposed approach, we built a database made of multiple day-synchronized S2 and S3 operational products from a study area in Extremadura (Spain). The conducted experimental comparison, including multiple state-of-the-art regression and deep learning models, shows the statistically significant advantages of the presented framework. The codes of this work will be made available at https://github.com/rufernan/3CRN.

Published
2021

47. Consistency Center-Based Deep Cross-Modal Hashing for Multisource Remote Sensing Image Retrieval

Author

Sun, Yuxi, Ye, Yunming, Kang, Jian, Fernandez-Beltran, Ruben, Li, Xutao, Xiong, Zhenyu, Huang, Xu, and Plaza, Antonio

Abstract

Cross-modal hashing aims to retrieve similar images from large-scale Earth observation (EO) data archives, which typically contain multiple satellite sources of remote sensing (RS) images. However, existing cross-modal hashing methods primarily focus on dual-source RS images and often face two main limitations when retrieving multisource RS images. First, these methods exhibit significant redundancy as they require handling all possible dual-source combinations in multisource RS images. Second, they often rely on pairwise or triplet image sources to construct objective functions, which are not significantly effective in reducing the discrepancies among multiple RS image sources. To address these limitations, we propose a novel consistency center-based deep cross-modal hashing method for multisource RS image retrieval called ${\texttt {C}^{2}\texttt {Hash}}$ . Our ${\texttt {C}^{2}\texttt {Hash}}$ employs a multibranch hashing network to directly encode multisource RS images into unified hash codes, thereby offering higher processing efficiency. Furthermore, ${\texttt {C}^{2}\texttt {Hash}}$ introduces consistency centers to construct a novel objective function. The consistency center represents the shared semantic features among similar multisource RS images and is generated by a label hashing network. The objective function encourages similar multisource RS images to approach the same consistency center to align all image sources in a unified Hamming space. Our method can effectively reduce the discrepancies across multiple image sources and generate unified hash codes. To evaluate its effectiveness, we construct a new multisource RS image dataset called MSRSI, comprising five different types of image sources. We conduct comprehensive experiments to demonstrate the superior performance of our method on the MSRSI dataset (https://github.com/sunyuxi/C2Hash).

Published
2023
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