About: Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping Goto Sponge NotDistinct Permalink

An Entity of Type : wikibase:Item, within Data Space : wikidata.demo.openlinksw.com associated with source document(s)

scholarly article by Weijia Li et al published 31 October 2016 in International Journal of Remote Sensing

Attributes	Values
rdf:type	Item
description	wetenschappelijk artikel (nl) article scientifique publié en 2016 (fr) наукова стаття, опублікована в жовтні 2016 (uk) im Oktober 2016 veröffentlichter wissenschaftlicher Artikel (de) scholarly article by Weijia Li et al published 31 October 2016 in International Journal of Remote Sensing (en)
publication date	wds:Q58068511-3D78B742-AB7B-4C80-92EB-77150301CF17
publication date	2016-10-31 00:00:00Z (xsd:dateTime)
cites work	wds:Q58068511-1B3C76D5-8ECB-4062-AE31-BE2F46D136CF wds:Q58068511-4522F6DA-3C16-4F3B-8A6A-C753542EA77A wds:Q58068511-63730220-7FB1-435F-B4AF-9AB1CFFFD8B1 wds:Q58068511-9C91E92F-C630-4789-BC3C-78C1F86703D2 wds:Q58068511-C89A648E-A03A-44F5-BA5C-227BF480918E wds:Q58068511-CBBB491C-709C-44C8-8F51-00FF576E5677 wds:Q58068511-E3B009BB-2965-4205-A7BC-815E056ECD76
cites work	Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition Meta-discoveries from a synthesis of satellite-based land-cover mapping research A multi-resolution global land cover dataset through multisource data aggregation Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data An all-season sample database for improving land-cover mapping of Africa with two classification schemes
author name string	wds:Q58068511-1198C84D-F240-4601-8E94-337BA4E28691 wds:Q58068511-67F81BE0-C577-48C0-AAE2-06410464EF18 wds:Q58068511-855E4536-C634-4246-97B6-3EC701426561 wds:Q58068511-970442C2-3805-441F-AA18-F3358EA76881 wds:Q58068511-DA82F9D3-0B35-416E-8162-DFF78C717227
author name string	Congcong Li Weijia Li Haohuan Fu Nicholas Clinton Duole Feng
rdfs:label	Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping (en) Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping (nl)
skos:prefLabel	Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping (en) Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping (nl)
name	Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping (en) Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping (nl)
author	wds:Q58068511-A007860E-C895-4E9A-8588-0C1E008DE175 wds:Q58068511-A541BA57-7BC3-4BC1-8D3F-7C3C9943F404
author	Peng Gong Le Yu
title	wds:Q58068511-238A996D-56E4-40C4-B054-F80FBE7ED1C1
title	Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping (en)
page(s)	wds:Q58068511-DBE561BC-0F73-4DFD-AB30-8A6304065DBF
page(s)	5632-5646
instance of	wds:Q58068511-5085BA69-63A1-4D11-9D65-04BCE93874EA
instance of	scholarly article
main subject	wds:Q58068511-265D2141-DB4F-4FB5-8DC3-B9402B89C204 wds:Q58068511-572BF55B-A186-4A25-B05C-4EABF7AD9EA8 wds:Q58068511-E4590ECC-17B4-4BDD-8289-19AFC83AFD19 wds:Q58068511-F88D3243-C27E-4BA6-BBF9-5BEED538A6C8
main subject	land cover remote sensing deep learning image classification
published in	wds:Q58068511-47985FE4-37E4-434D-B511-7418DC89971A
published in	International Journal of Remote Sensing
issue	wds:Q58068511-F0602F51-BE68-4430-B40E-69ABB4A6C3C6
volume	wds:Q58068511-2BCF90F9-E082-4CED-8E26-9D71816E34B2
issue	23
volume	37
DOI	wds:Q58068511-D44D8BA9-2BFC-4CC6-94B1-EEE1AB3095F5
DOI	http://dx.doi.org/10.1080/01431161.2016.1246775
DOI	10.1080/01431161.2016.1246775
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q58068511
is cites work of	Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images Mapping Early, Middle and Late Rice Extent Using Sentinel-1A and Landsat-8 Data in the Poyang Lake Plain, China.
is cites work of	wds:Q47720828-5CEF0EDD-26B0-4B22-ACD4-02266FDCD9D9 wds:Q58068492-3A61146D-99A9-4C11-B15A-5AA6868B903D

Faceted Search & Find service v1.16.117 as of May 05 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3239 as of May 5 2024, on Linux (x86_64-centos_6-linux-gnu), Single-Server Edition (378 GB total memory, 183 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software