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Akademický článek

COMPARISON OF CONVOLUTIONAL NEURAL NETWORKS FOR CLOUDY OPTICAL IMAGES RECONSTRUCTION FROM SINGLE OR MULTITEMPORAL JOINT SAR AND OPTICAL IMAGES

Autor: R. Cresson, N. Narçon, R. Gaetano, A. Dupuis, Y. Tanguy, S. May, B. Commandré

Publikováno v: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B3-2022, Pp 1317-1326 (2022)

With the increasing availability of optical and synthetic aperture radar (SAR) images thanks to the Sentinel constellation, and the explosion of deep learning, new methods have emerged in recent years to tackle the reconstruction of optical images th

Externí odkaz: https://doaj.org/article/1ad6f462b8e543d7992163421ac2abc0

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Akademický článek

Harmonized in situ datasets for agricultural land use mapping and monitoring in tropical countries

Publikováno v: Earth System Science Data, Vol 13, Pp 5951-5967 (2021)

The availability of crop type reference datasets for satellite image classification is very limited for complex agricultural systems as observed in developing and emerging countries. Indeed, agricultural land use is very dynamic, agricultural censuse

Externí odkaz: https://doaj.org/article/9eeb9d76d10f4c3196779cc5b9778e6d

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Akademický článek

GENERALIZED KNOWLEDGE DISTILLATION FOR MULTI-SENSOR REMOTE SENSING CLASSIFICATION: AN APPLICATION TO LAND COVER MAPPING

Autor: D. Ienco, Y. J. E. Gbodjo, R. Gaetano, R. Interdonato

Publikováno v: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 997-1003 (2020)

Due to the proliferation of Earth Observation programmes, information at different spatial, spectral and temporal resolution is collected by means of various sensors (optical, radar, hyperspectral, LiDAR, etc.). Despite such abundance of information,

Externí odkaz: https://doaj.org/article/f03d344d7acf4219a0c4698be2ba9cc1

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