Search Results for author: Luis Gómez-Chova

Found 9 papers, 1 papers with code

Convolutional Neural Networks for Multispectral Image Cloud Masking

no code implementations9 Dec 2020 Gonzalo Mateo-García, Luis Gómez-Chova, Gustau Camps-Valls

Convolutional neural networks (CNN) have proven to be state of the art methods for many image classification tasks and their use is rapidly increasing in remote sensing problems.

BIG-bench Machine Learning Classification +2

Pattern Recognition Scheme for Large-Scale Cloud Detection over Landmarks

no code implementations8 Dec 2020 Adrián Pérez-Suay, Julia Amorós-López, Luis Gómez-Chova, Jordi Muñoz-Marí, Dieter Just, Gustau Camps-Valls

Landmark recognition and matching is a critical step in many Image Navigation and Registration (INR) models for geostationary satellite services, as well as to maintain the geometric quality assessment (GQA) in the instrument data processing chain of Earth observation satellites.

Cloud Detection Landmark Recognition

Randomized kernels for large scale Earth observation applications

no code implementations7 Dec 2020 Adrián Pérez-Suay, Julia Amorós-López, Luis Gómez-Chova, Valero Laparra, Jordi Muñoz-Marí, Gustau Camps-Valls

Dealing with land cover classification of the new image sources has also turned to be a complex problem requiring large amount of memory and processing time.

Classification General Classification +4

Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection

1 code implementation10 Jun 2020 Gonzalo Mateo-García, Valero Laparra, Dan López-Puigdollers, Luis Gómez-Chova

In addition, the training of the proposed adversarial domain adaptation model can be modified to improve the performance in a specific remote sensing application, such as cloud detection, by including a dedicated term in the cost function.

Cloud Detection Domain Adaptation +1

Fair Kernel Learning

no code implementations16 Oct 2017 Adrián Pérez-Suay, Valero Laparra, Gonzalo Mateo-García, Jordi Muñoz-Marí, Luis Gómez-Chova, Gustau Camps-Valls

It has been shown that not including sensitive features that bias fairness, such as gender or race, is not enough to mitigate the discrimination when other related features are included.

BIG-bench Machine Learning Dimensionality Reduction +2

Optimized Kernel Entropy Components

no code implementations9 Mar 2016 Emma Izquierdo-Verdiguier, Valero Laparra, Robert Jenssen, Luis Gómez-Chova, Gustau Camps-Valls

Results show that 1) OKECA returns projections with more expressive power than KECA, 2) the most successful rule for estimating the kernel parameter is based on maximum likelihood, and 3) OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.

Density Estimation

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