Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion ...
Land cover datasets are essential for modeling Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, and finding quality satellite remote sensing datasets...
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Friedrich-Schiller-Universität Jena
2018
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Online Access: | https://dx.doi.org/10.22032/dbt.37838 https://www.db-thueringen.de/receive/dbt_mods_00037838 |
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ftdatacite:10.22032/dbt.37838 2023-12-31T10:02:42+01:00 Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion ... Kumar, Jitendra Langford, Zachary Hoffman, Forrest 2018 https://dx.doi.org/10.22032/dbt.37838 https://www.db-thueringen.de/receive/dbt_mods_00037838 en eng Friedrich-Schiller-Universität Jena https://www.db-thueringen.de/receive/dbt_mods_00037838?XSL.Transformer=mods https://www.db-thueringen.de/receive/dbt_mods_00037820 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 deep learning, arctic vegetation, remote sensing, multi-sensor fusion 004 570 580 590 600 630 ScholarlyArticle Text article-journal speech 2018 ftdatacite https://doi.org/10.22032/dbt.37838 2023-12-01T11:25:54Z Land cover datasets are essential for modeling Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, and finding quality satellite remote sensing datasets to produce such maps is difficult due to cloud cover, polar darkness, and poor availability of high-resolution imagery. A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. The datasets from hyperspectral, multispectral, synthetic aperture radar (SAR) platforms, and terrain datasets were fused together using unsupervised and supervised classification techniques over a 343 km2 region to generate high-resolution (5 m) vegetation type maps. A unsupervised technique was developed to classify high-dimensional remote sensing datasets into cohesive clusters and a quantitative technique ... : ICEI 2018 : 10th International Conference on Ecological Informatics- Translating Ecological Data into Knowledge and Decisions in a Rapidly Changing World ... Audio Arctic Seward Peninsula Alaska DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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English |
topic |
deep learning, arctic vegetation, remote sensing, multi-sensor fusion 004 570 580 590 600 630 |
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deep learning, arctic vegetation, remote sensing, multi-sensor fusion 004 570 580 590 600 630 Kumar, Jitendra Langford, Zachary Hoffman, Forrest Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion ... |
topic_facet |
deep learning, arctic vegetation, remote sensing, multi-sensor fusion 004 570 580 590 600 630 |
description |
Land cover datasets are essential for modeling Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, and finding quality satellite remote sensing datasets to produce such maps is difficult due to cloud cover, polar darkness, and poor availability of high-resolution imagery. A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. The datasets from hyperspectral, multispectral, synthetic aperture radar (SAR) platforms, and terrain datasets were fused together using unsupervised and supervised classification techniques over a 343 km2 region to generate high-resolution (5 m) vegetation type maps. A unsupervised technique was developed to classify high-dimensional remote sensing datasets into cohesive clusters and a quantitative technique ... : ICEI 2018 : 10th International Conference on Ecological Informatics- Translating Ecological Data into Knowledge and Decisions in a Rapidly Changing World ... |
format |
Audio |
author |
Kumar, Jitendra Langford, Zachary Hoffman, Forrest |
author_facet |
Kumar, Jitendra Langford, Zachary Hoffman, Forrest |
author_sort |
Kumar, Jitendra |
title |
Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion ... |
title_short |
Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion ... |
title_full |
Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion ... |
title_fullStr |
Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion ... |
title_full_unstemmed |
Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion ... |
title_sort |
deep learning approach for mapping arctic vegetation using multi-sensor remote sensing fusion ... |
publisher |
Friedrich-Schiller-Universität Jena |
publishDate |
2018 |
url |
https://dx.doi.org/10.22032/dbt.37838 https://www.db-thueringen.de/receive/dbt_mods_00037838 |
genre |
Arctic Seward Peninsula Alaska |
genre_facet |
Arctic Seward Peninsula Alaska |
op_relation |
https://www.db-thueringen.de/receive/dbt_mods_00037838?XSL.Transformer=mods https://www.db-thueringen.de/receive/dbt_mods_00037820 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.22032/dbt.37838 |
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1786813505742045184 |