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Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images
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article
(en)
wetenschappelijk artikel
(nl)
наукова стаття, опублікована у вересні 2016
(uk)
im September 2016 veröffentlichter wissenschaftlicher Artikel
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publication date
wds:Q58101692-33418430-4971-4E6D-9986-4D975F1063C4
publication date
2016-09-01 00:00:00Z
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cites work
wds:Q58101692-2DB0EECC-46FA-425D-8358-CB5686D8D9FA
wds:Q58101692-2FCE31E8-ECBB-4752-A5AB-4611035949C1
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cites work
Random Forests
Geographic Object-Based Image Analysis - Towards a new paradigm
Remote sensing of aquatic vegetation: theory and applications.
Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery
Ecology of freshwater shore zones
When trends intersect: The challenge of protecting freshwater ecosystems under multiple land use and hydrological intensification scenarios
Maximum growing depth of submerged macrophytes in European lakes
author name string
wds:Q58101692-4BFC21D5-01AF-4A17-9AD2-CBDD7EB9DB5B
wds:Q58101692-A93BC5E3-6320-45CE-BEA4-1F84EB729356
author name string
Eva Husson
Heather Reese
rdfs:label
Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images
(en)
Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images
(nl)
skos:prefLabel
Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images
(en)
Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images
(nl)
name
Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images
(en)
Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images
(nl)
author
wds:Q58101692-D01ACF39-733B-464A-985F-4E87172BF553
author
Frauke Ecke
title
wds:Q58101692-04AE12BA-438F-4958-BA25-C0F9013ACEF0
title
Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images
(en)
page(s)
wds:Q58101692-A05A6C8B-B54B-44FC-8FC2-DDCDEA45168C
page(s)
724
instance of
wds:Q58101692-443FB076-9350-4AAE-A0C7-9087E2C60BF6
instance of
scholarly article
main subject
wds:Q58101692-261A1A75-0022-41E9-B9CA-48F527C2D049
wds:Q58101692-2D018FB9-40FC-48EE-BFE4-14A2B37C1B10
main subject
automation
image analysis
DBLP publication ID
wds:Q58101692-384E1826-4161-4905-8C48-AA2CD4C842EC
DBLP publication ID
https://dblp.org/rec/journals/remotesensing/HussonER16
DBLP publication ID
journals/remotesensing/HussonER16
published in
wds:Q58101692-91146E60-E67C-41F2-BD19-CBE20FCF9A16
published in
Remote Sensing
issue
wds:Q58101692-7AD98711-822C-49DD-AB7B-71BDB8125674
volume
wds:Q58101692-C54783A6-A772-45E3-BB6B-FEB0F3F25EF6
issue
9
volume
8
DOI
wds:Q58101692-DF8692C6-1303-44B4-A8E3-4C65850D4EF5
DOI
http://dx.doi.org/10.3390/RS8090724
DOI
10.3390/RS8090724
copyright license
wds:Q58101692-5fb6e1ce-30af-4f1d-8279-656180c7b51c
copyright status
wds:Q58101692-562f5b68-93de-4807-b4d3-791977804ad6
copyright license
Creative Commons Attribution 4.0 International
copyright status
copyrighted
ADS bibcode
wds:Q58101692-5FD27170-47BE-4D3C-A28E-317E6610F0A0
ADS bibcode
2016RemS....8..724H
is
about
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https://www.wikidata.org/wiki/Special:EntityData/Q58101692
is
cites work
of
Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation
Comparing Pixel- and Object-Based Approaches in Effectively Classifying Wetland-Dominated Landscapes.
Remotely sensed rivers in the Anthropocene: state of the art and prospects
Location, location, location: considerations when using lightweight drones in challenging environments
is
cites work
of
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wds:Q55017278-3146C858-31B9-41F1-8D49-48903B2A5134
wds:Q98071996-A16E50A2-AC6B-4B1A-A751-DB110E85C257
wds:Q57917720-8B94CA3A-35BB-42DD-A6D8-0D673519DB38
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