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Dipòsit Digital de Documents de la UAB 72 registres trobats  anterior11 - 20següentfinal  anar al registre: La cerca s'ha fet en 0.01 segons. 
11.
15 p, 1.7 MB G-reqs, a New Model Proposal for Capturing and Managing In Situ Data Requirements : First Results in the Context of the Group on Earth Observations / Maso, Joan (Centre de Recerca Ecològica i d'Aplicacions Forestals) ; Brobia, Alba (Centre de Recerca Ecològica i d'Aplicacions Forestals) ; Voidrot, Marie F. (Open Geospatial Consortium Europe) ; Zabala Torres, Alaitz (Universitat Autònoma de Barcelona. Departament de Geografia) ; Serral Montoro, Ivette (Centre de Recerca Ecològica i d'Aplicacions Forestals)
In the field of Earth observation, the importance of in situ data was recognized by the Group on Earth Observations (GEO) in the Canberra Declaration in 2019. The GEO community focuses on three global priority engagement areas: the United Nations 2030 Agenda for Sustainable Development, the Paris Agreement, and the Sendai Framework for Disaster Risk Reduction. [...]
2023 - 10.3390/rs15061589
Remote sensing (Basel), Vol. 15, Issue 6 (March 2023) , art. 1589  
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12.
6 p, 488.4 KB Reducing data dependencies in the feedback loop of the CCSDS 123.0-B-2 predictor / Sánchez, Antonio José (Universidad de Las Palmas de Gran Canaria. Instituto Universitario de Microelectrónica Aplicada) ; Blanes Garcia, Ian (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions) ; Barrios, Yubal (Universidad de Las Palmas de Gran Canaria. Instituto Universitario de Microelectrónica Aplicada) ; Hernández-Cabronero, Miguel (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions) ; Bartrina Rapesta, Joan (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions) ; Serra Sagristà, Joan (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions) ; Sarmiento, Roberto (Universidad de Las Palmas de Gran Canaria. Instituto Universitario de Microelectrónica Aplicada)
On-board multi- and hyperspectral instruments acquire large volumes of data that need to be processed with the limited computational and storage resources. In this context, the CCSDS 123. 0-B-2 standard emerges as an interesting option to compress multi- and hyperspectral images on-board satellites, supporting both lossless and near-lossless compression with low complexity and reduced power consumption. [...]
2022 - 10.1109/LGRS.2022.3213975
IEEE Geoscience and Remote Sensing Letters, Vol. 19 (October 2022) , art. 6014505  
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13.
10 p, 522.1 KB Hyperspectral camera system : acquisition and analysis / Brelstaff, Gavin J. (National Remote Sensing Ctr. Ltd. (Regne Unit)) ; Parraga, Carlos Alejandro (University of Bristol) ; Troscianko, Tom (University of Bristol) ; Carr, Derek (University of Bristol)
A low-cost, portable, video-camera system built by University of Bristol for the UK-DRA, RARDE Fort Halstead, permits in-field acquisition of terrestrial hyper-spectral image sets. Each set is captured as a sequence of thirty-one images through a set of different interference filters which span the visible spectrum, at 10 nm intervals: effectively providing a spectrogram of 256 by 256 pixels. [...]
Society of Photo-Optical Instrumentation Engineers, cop. 1995 (Proceedings of SPIE ; 2587) - 10.1117/12.226819
Geographic Information Systems, Photogrammetry, and Geological/Geophysical Remote Sensing (Paris, 25-28 September 1995), 1995, p. 150-159  
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14.
18 p, 2.4 MB Machine learning for mineral identification and ore estimation from hyperspectral imagery in tin-tungsten deposits : Simulation under indoor conditions / Lobo, Agustin (Geosciences Barcelona) ; Garcia, Emma (Lithica) ; Barroso, Gisela (Universitat Autònoma de Barcelona) ; Martí, David (Lithica) ; Fernandez-Turiel, Jose-Luis (Geosciences Barcelona) ; Ibañez-Insa, Jordi (Geosciences Barcelona)
This study aims to assess the feasibility of delineating and identifying mineral ores from hyperspectral images of tin-tungsten mine excavation faces using machine learning classification. We compiled a set of hand samples of minerals of interest from a tin-tungsten mine and analyzed two types of hyperspectral images: (1) images acquired with a laboratory set-up under close-to-optimal conditions, and (2) a scan of a simulated mine face using a field set-up, under conditions closer to those in the gallery. [...]
2021 - 10.3390/rs13163258
Remote sensing (Basel), Vol. 13, Issue 16 (August 2021) , art. 3258  
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15.
5 p, 210.7 KB Editorial to special issue "remote sensing data compression" / Vozel, Benoit (University of Rennes 1. Engineering School of Applied Sciences and Technology) ; Lukin, Vladimir (National Aerospace University. Department of Information and Communication Technologies) ; Serra Sagristà, Joan (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and/or to customers. Due to different limitations, compression has to be applied on-board and/or on-the-ground. [...]
2021 - 10.3390/rs13183727
Remote sensing (Basel), Vol. 13, Issue 18 (September 2021) , art. 3727  
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16.
21 p, 7.8 MB Fusing Landsat and SAR Data for Mapping Tropical Deforestation through Machine Learning Classification and the PVts-β Non-Seasonal Detection Approach / Tarazona Coronel, Yonatan (Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions) ; Zabala Torres, Alaitz (Universitat Autònoma de Barcelona. Departament de Geografia) ; Pons, Xavier (Universitat Autònoma de Barcelona. Departament de Geografia) ; Broquetas, Antoni (Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions) ; Nowosad, Jakub (Adam Mickiewicz University (Polònia). Institute of Geoecology and Geoinformation) ; Zurqani, Hamdi A. (Clemson University (Estats Units). Department of Forestry and Environmental Conservation)
This article focuses on mapping tropical deforestation using time series and machine learning algorithms. Before detecting changes in the time series, we reduced seasonality using Photosynthetic Vegetation (PV) index fractions obtained from Landsat images. [...]
2021 - 10.1080/07038992.2021.1941823
Canadian Journal of Remote Sensing, Vol. 47, núm. 5 (2021) , p. 677-696  
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17.
11 p, 1.8 MB Assess citizen science based land cover maps with remote sensing products : the Ground Truth 2.0 data quality tool / Maso, Joan (Centre de Recerca Ecològica i d'Aplicacions Forestals) ; Julià, Núria (Centre de Recerca Ecològica i d'Aplicacions Forestals) ; Zabala Torres, Alaitz (Universitat Autònoma de Barcelona. Departament de Geografia) ; Prat Carrió, Ester (Centre de Recerca Ecològica i d'Aplicacions Forestals) ; Van der Kwast, Johannes (IHE Delft Institute for Water Education, Department of Water Resources and Ecosystems) ; Domingo-Marimon, Cristina (Centre de Recerca Ecològica i d'Aplicacions Forestals)
One of the main concerns in adopting citizen science is data quality. Derived products inherit intrinsic limitations of the capture methodology as well as the uncertainties in observations. OpenStreetMap tools are designed to minimize uncertainties in positional accuracy by ensuring a good co-registration of the observations with imagery or direct use of GPS. [...]
2020 - 10.1117/12.2570814
Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020), Vol. 11524 (2020) , p. 586-595  
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18.
18 p, 2.3 MB Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S leaf area index products in maize crops / Yu, Huinan (Southwest Jiaotong University. Faculty of Geosciences and Environmental Engineering) ; Yin, Gaofei (Centre de Recerca Ecològica i d'Aplicacions Forestals) ; Liu, Guoxiang (Southwest Jiaotong University. Faculty of Geosciences and Environmental Engineering) ; Ye, Yuanxin (Southwest Jiaotong University. Faculty of Geosciences and Environmental Engineering) ; Qu, Yonghua (Chinese Academy of Sciences. Institute of Remote Sensing and Digital Earth) ; Xu, Baodong (Huazhong Agricultural University. Macro Agriculture Research Institute) ; Verger, Aleixandre (Centre de Recerca Ecològica i d'Aplicacions Forestals)
We proposed a direct approach to validate hectometric and kilometric resolution leaf area index (LAI) products that involved the scaling up of field-measured LAI via the validation and recalibration of the decametric Sentinel-2 LAI product. [...]
2021 - 10.3390/rs13224529
Remote sensing (Basel), Vol. 13, issue 22 (Nov. 2021) , art 4529  
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19.
9 p, 4.0 MB Evaluation and normalization of topographic effects on vegetation indices / Chen, Rui (Southwest Jiaotong University) ; Yin, Gaofei (Centre de Recerca Ecològica i d'Aplicacions Forestals) ; Liu, Guoxiang (Southwest Jiaotong University) ; Li, Jing (Chinese Academy of Sciences. Institute of Remote Sensing and Digital Earth) ; Verger, Aleixandre (Centre de Recerca Ecològica i d'Aplicacions Forestals)
The normalization of topographic effects on vegetation indices (VIs) is a prerequisite for their proper use in mountainous areas. We assessed the topographic effects on the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the soil adjusted vegetation index (SAVI), and the near-infrared reflectance of terrestrial vegetation (NIRv) calculated from Sentinel-2. [...]
2020 - 10.3390/rs12142290
Remote sensing (Basel), Vol. 12, Issue 14 (July 2020) , art. 2290  
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20.
6 p, 1.5 MB A threshold method for robust and fast estimation of land-surface phenology using Google Earth Engine / Descals, Adrià (Centre de Recerca Ecològica i d'Aplicacions Forestals) ; Verger, Aleixandre (Centre de Recerca Ecològica i d'Aplicacions Forestals) ; Yin, Gaofei (Centre de Recerca Ecològica i d'Aplicacions Forestals) ; Peñuelas, Josep (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Cloud-based platforms are changing the way of analyzing remotely sensed data by providing high computational power and rapid access to massive volumes of data. Several types of studies use cloud-based platforms for global-scale analyses, but the number of land-surface phenology (LSP) studies that use cloud-based platforms is low. [...]
2021 - 10.1109/JSTARS.2020.3039554
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14 (2021) , p. 601-606  
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Dipòsit Digital de Documents de la UAB : 72 registres trobats   anterior11 - 20següentfinal  anar al registre:
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