| PhD THESES SUPERVISED |
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| 2022 | "Change detection machine learning algorithms for cloud masking of remote sensing image time series" Dan López Puigdollers. Luis Gómez-Chova (Adv.), 2019-2022. |
| 2021 | "Machine Learning for Cloud Masking of Multispectral Satellite Images" Gonzalo Mateo García. Luis Gómez-Chova (Adv.) 2018-2021. |
| 2014 | "Kernel feature extraction methods for remote sensing data analysis" Emma Izquierdo-Verdiguier. Luis Gómez-Chova and Gustau Camps Valls (Advs.), Cum Laude, 25/07/2014. |
| 2012 | "Multi-resolution Spatial Unmixing for MERIS and Landsat Image Fusion" Julia Amorós López. Luis Gómez-Chova and Javier Calpe Maravilla (Advs.), Cum Laude, 14/03/2012. |
| My PhD THESIS |
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"Cloud screening algorithm for MERIS and CHRIS multispectral sensors" Luis Gómez-Chova, Universitat de Valencia, 2008. J. Calpe-Maravilla and G. Camps-Valls (advisors). ISBN: 978-84-370-7354-5. Abstract: "Earth Observation systems monitor our Planet by measuring, at different wavelengths, the electromagnetic radiation that is reflected by the surface, crosses the atmosphere, and reaches the sensor at the satellite platform. In this process, clouds are one of the most important components of the Earth's atmosphere affecting the quality of the measured electromagnetic signal and, consequently, the properties retrieved from these signals. This Thesis faces the challenging problem of cloud screening in multispectral and hyperspectral images acquired by space-borne sensors working in the visible and near-infrared range of the electromagnetic spectrum. The main objective is to provide new operational cloud screening tools for the derivation of cloud location maps from these sensors' data. Moreover, the method must provide cloud abundance maps -instead of a binary classification- to better describe clouds (abundance, type, height, subpixel coverage), thus allowing the retrieval of surface biophysical parameters from satellite data acquired over land and ocean. In this context, this Thesis is intended to support the growing interest of the scientific community in two multispectral sensors on board two satellites of the European Space Agency (ESA). The first one is the MEdium Resolution Imaging Spectrometer (MERIS), placed on board the biggest environmental satellite ever launched, ENVISAT. The second one is the Compact High Resolution Imaging Spectrometer (CHRIS) hyperspectral instrument, mounted on board the technology demonstration mission PROBA (Project for On-Board Autonomy). The proposed cloud screening algorithm takes advantage of the high spectral and radiometric resolution of MERIS, and of the high number of spectral bands of CHRIS, as well as the specific location of some bands (e.g., oxygen and water vapor absorption bands) to increase the cloud detection accuracy. To attain this objective, advanced pattern recognition and machine learning techniques to detect clouds are specifically developed in the frame of this Thesis. First, a feature extraction based on meaningful physical facts is carried out in order to provide informative inputs to the algorithms. Then, the cloud screening algorithm is conceived trying to make use of the wealth of unlabeled samples in Earth Observation images, and thus unsupervised and semi-supervised learning methods are explored. Results show that applying unsupervised clustering methods over the whole image allows us to take advantage of the wealth of information and the high degree of spatial and spectral correlation of the image pixels, while semi-supervised learning methods offer the opportunity of exploiting also the available labeled samples." |
| JOURNAL PUBLICATIONS DIRECTLY RELATED WITH MY THESIS "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." |
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| 2010 | |
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"Mean Map Kernel Methods for Semisupervised Cloud
Classification" Luis Gómez-Chova, Gustavo Camps-Valls, Lorenzo Bruzzone and Javier Calpe-Maravilla. IEEE Transactions on Geoscience and Remote Sensing, 48(1), pp. 207-220, 2010. |
| 2008 | |
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"Correction of systematic spatial noise in push-broom hyperspectral sensors: Application to CHRIS/PROBA images" Luis Gomez-Chova, Luis Alonso, Luis Guanter, Gustavo Camps-Valls, Javier Calpe and Jose Moreno, Applied Optics, Vol. 47, No. 28, 1 Oct. 2008, Pages F46-F60. |
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"Semisupervised Image Classification with
Laplacian Support Vector Machines." L. Gomez, G. Camps-Valls, J. Muñoz-Marí and J. Calpe-Maravilla IEEE Geoscience and Remote Sensing Letters, Volume 5, Issue 3, July 2008 Page(s):336-340. |
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"Kernel-based framework for multi-temporal and multi-source remote sensing data classification and change detection."
G. Camps-Valls, L. Gómez-Chova, J. Muñoz-Marí, M. Martínez-Ramón, and J. L. Rojo-Álvarez. IEEE Transactions on Geoscience and Remote Sensing, Volume 46, Issue 6, June 2008 Pages 1822-1835. |
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"Coupled retrieval of aerosol optical thickness, columnar water vapor and surface reflectance maps from ENVISAT/MERIS data over land". Luis Guanter, Luis Gómez-Chova, Jose Moreno. Remote Sensing of Environment, Volume 112, Issue 6, June 2008, Pages 2898-2913. |
| 2007 | |
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"Cloud Screening Algorithm for
ENVISAT/MERIS Multispectral Images." Luis Gómez-Chova, Gustavo Camps-Valls, Javier Calpe, Luis Guanter, José Moreno IEEE Transactions on Geoscience and Remote Sensing, 45(12), Part 2, pp. 4105-4118. 2007. |
| 2006 | |
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"Composite kernels for
hyperspectral image classification" Gustavo Camps-Valls, Luis Gomez-Chova, Jordi Muñoz-Marí, Joan Vila-Francés, and Javier Calpe-Maravilla IEEE Geoscience and Remote Sensing Letters, Jan, 2006. |
| 2004 |
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"Robust
Support Vector Method for
Hyperspectral
Data Classification and Knowledge Discovery". G. Camps-Valls, L. Gómez-Chova, J. Calpe-Maravilla, J. D. Martín-Guerrero, E. Soria-Olivas, L. Alonso-Chordá, José Moreno. IEEE Transactions on Geoscience and Remote Sensing, July 2004, Vol.42, Issue 7, pp. 1530-1542. |
(c) copyright. Luis Gomez-Chova.