Zobrazeno 1 - 10
of 81
pro vyhledávání: '"per-pixel classification"'
Publikováno v:
Geo-spatial Information Science, Vol 24, Iss 2, Pp 215-227 (2021)
LiDAR data are becoming increasingly available, which has opened up many new applications. One such application is crop type mapping. Accurate crop type maps are critical for monitoring water use, estimating harvests and in precision agriculture. The
Externí odkaz:
https://doaj.org/article/68f5156eb40e4f89b24d0a7a55632449
Publikováno v:
Biotechnologie, Agronomie, Société et Environnement, Vol 22, Iss 3, Pp 172-187 (2018)
Description of the subject. Understanding the current situation and evolution of forests is essential for a sustainable management plan that maintains forests' ecological and socio-economic functions. Remote sensing is a helpful tool in developing th
Externí odkaz:
https://doaj.org/article/53c8d9d827464dd397bd4e6c7d5851f8
Autor:
Lucie Kupková, Lucie Červená, Renáta Suchá, Lucie Jakešová, Bogdan Zagajewski, Stanislav Březina, Jana Albrechtová
Publikováno v:
European Journal of Remote Sensing, Vol 50, Iss 1, Pp 29-46 (2017)
The aim of this study was to evaluate and compare suitability of aerial hyperspectral data (AISA Dual and APEX sensors) and Sentinel-2A data for classification of tundra vegetation cover in the Krkonoše Mts. National Park. We compared classification
Externí odkaz:
https://doaj.org/article/9be87133fe9e4211b8b1c8410c7cc998
Publikováno v:
Acta Universitatis Carolinae Geographica, Vol 51, Iss 1, Pp 113-129 (2016)
This paper compares suitability of multispectral data with different spatial and spectral resolutions for classifications of vegetation above the tree line in the Krkonoše Mts. National Park. Two legends were proposed: the detailed one with twelve c
Externí odkaz:
https://doaj.org/article/2744846fc9ae46d3b864ebac4f0544e5
A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery
Autor:
Chengming Zhang, Yingjuan Han, Feng Li, Shuai Gao, Dejuan Song, Hui Zhao, Keqi Fan, Ya’nan Zhang
Publikováno v:
Remote Sensing, Vol 11, Iss 6, p 619 (2019)
When the spatial distribution of winter wheat is extracted from high-resolution remote sensing imagery using convolutional neural networks (CNN), field edge results are usually rough, resulting in lowered overall accuracy. This study proposed a new p
Externí odkaz:
https://doaj.org/article/86cca5e609a24232ae11f2d74728e043
Akademický článek
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Publikováno v:
Geo-spatial Information Science, Vol 24, Iss 2, Pp 215-227 (2021)
LiDAR data are becoming increasingly available, which has opened up many new applications. One such application is crop type mapping. Accurate crop type maps are critical for monitoring water use, estimating harvests and in precision agriculture. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0297f1f7348ed0bd3c08a1c1452ad3a6
https://doi.org/10.1080/10095020.2020.1782776
https://doi.org/10.1080/10095020.2020.1782776
Publikováno v:
Remote Sensing, Vol 9, Iss 12, p 1328 (2017)
Using deep learning to improve the capabilities of high-resolution satellite images has emerged recently as an important topic in automatic classification. Deep networks track hierarchical high-level features to identify objects; however, enhancing t
Externí odkaz:
https://doaj.org/article/4b3278eeb5a142ed95ece811190e16e7
Publikováno v:
Remote Sensing, Vol 8, Iss 4, p 329 (2016)
The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural netwo
Externí odkaz:
https://doaj.org/article/2bbb374a3ba74f30b12260a059f67114
© 2020 Elsevier B.V. Accurately identifying cloud types in images has multiple uses from meteorological science to computer graphics, especially as clouds are a major factor influencing atmospheric radiative transport. Understanding which cloud type
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7dd81c8047754e9e5268053007a77638
https://uwe-repository.worktribe.com/file/6555166/1/PPCC
https://uwe-repository.worktribe.com/file/6555166/1/PPCC
