Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Valero Laparra"'
Publikováno v:
Mathematics, Vol 12, Iss 9, p 1406 (2024)
Volatility estimation and quantile regression are relevant active research areas in statistics, machine learning and econometrics. In this work, we propose two procedures to estimate the local variances in generic regression problems by using kernel
Externí odkaz:
https://doaj.org/article/892c938c3ebc43ff852418394ce036ce
A Scalable Reduced-Complexity Compression of Hyperspectral Remote Sensing Images Using Deep Learning
Autor:
Sebastià Mijares i Verdú, Johannes Ballé, Valero Laparra, Joan Bartrina-Rapesta, Miguel Hernández-Cabronero, Joan Serra-Sagristà
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4422 (2023)
Two key hurdles to the adoption of Machine Learning (ML) techniques in hyperspectral data compression are computational complexity and scalability for large numbers of bands. These are due to the limited computing capacity available in remote sensing
Externí odkaz:
https://doaj.org/article/c02e616a385d421491d0eb22c29ec1e1
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 747-761 (2021)
The number of Earth observation satellites carrying optical sensors with similar characteristics is constantly growing. Despite their similarities and the potential synergies among them, derived satellite products are often developed for each sensor
Externí odkaz:
https://doaj.org/article/d3e231c15e24495abba38d15ac89c804
Autor:
Jose Antonio Padron-Hidalgo, Adrian Perez-Suay, Fatih Nar, Valero Laparra, Gustau Camps-Valls
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 5480-5488 (2020)
Detecting anomalous changes in remote sensing images is a challenging problem, where many approaches and techniques have been presented so far. We rely on the standard field of multivariate statistics of diagnostic measures, which are concerned about
Externí odkaz:
https://doaj.org/article/646f6da95a754566951378dad8dc926d
Publikováno v:
PLoS ONE, Vol 16, Iss 2, p e0246775 (2021)
[This corrects the article DOI: 10.1371/journal.pone.0235885.].
Externí odkaz:
https://doaj.org/article/5c4efc52f7a341d6bbb1b3296b92711c
Publikováno v:
PLoS ONE, Vol 15, Iss 10, p e0235885 (2020)
Kernel methods are powerful machine learning techniques which use generic non-linear functions to solve complex tasks. They have a solid mathematical foundation and exhibit excellent performance in practice. However, kernel machines are still conside
Externí odkaz:
https://doaj.org/article/ce6147816a024ed18359858825775c21
Publikováno v:
PLoS ONE, Vol 9, Iss 2, p e86481 (2014)
Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of prima
Externí odkaz:
https://doaj.org/article/212dbfcbaf8c409da8a274dccbfb0d8e
Autor:
Maria Pilar Cendrero-Mateo, Shari Van Wittenberghe, Valero Laparra, Uwe Rascher, Shirley A. Papuga, Guillermo Ponce-Campos, Jose F. Moreno
In this study, we address two relevant gaps when monitoring plant photosynthesis using remote sensing techniques; these are i) assess the seasonal trends and relationships observed between photosynthesis, optical vegetation indices, and chlorophyll f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d3d7907439c1f779048be9ee5422c1c
https://doi.org/10.5194/egusphere-egu23-120
https://doi.org/10.5194/egusphere-egu23-120
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-10
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-10
Anomaly detection (AD) is a field of intense research in remote sensing (RS) image processing. Identifying low probability events in RS images is a challenging problem given the high dimensionality of the data, especially when no (or little) informat