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Land Use Land Cover Labeling of GLOBE Images Using a Deep Learning Fusion Model
Most of the land use land cover classification methods presented in the literature have been conducted using satellite remote sensing images. High-resolution aerial imagery is now being used for land cover classification. The Global Learning and Observations to Benefit, the Environment land cover im...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503776/ https://www.ncbi.nlm.nih.gov/pubmed/36146242 http://dx.doi.org/10.3390/s22186895 |
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author | Manzanarez, Sergio Manian, Vidya Santos, Marvin |
author_facet | Manzanarez, Sergio Manian, Vidya Santos, Marvin |
author_sort | Manzanarez, Sergio |
collection | PubMed |
description | Most of the land use land cover classification methods presented in the literature have been conducted using satellite remote sensing images. High-resolution aerial imagery is now being used for land cover classification. The Global Learning and Observations to Benefit, the Environment land cover image database, is created by citizen scientists worldwide who use their handheld cameras to take a set of six images per land cover site. These images have clutter due to man-made objects, and the pixel uncertainties result in incorrect labels. The problem of accurate labeling of these land cover images is addressed. An integrated architecture that combines Unet and DeepLabV3 for initial segmentation, followed by a weighted fusion model that combines the segmentation labels, is presented. The land cover images with labels are used for training the deep learning models. The fusion model combines the labels of five images taken from the north, south, east, west, and down directions to assign a unique label to the image sets. 2916 GLOBE images have been labeled with land cover classes using the integrated model with minimal human-in-the-loop annotation. The validation step shows that our architecture of labeling the images results in 90.97% label accuracy. Our fusion model can be used for labeling large databases of land cover classes from RGB images. |
format | Online Article Text |
id | pubmed-9503776 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95037762022-09-24 Land Use Land Cover Labeling of GLOBE Images Using a Deep Learning Fusion Model Manzanarez, Sergio Manian, Vidya Santos, Marvin Sensors (Basel) Article Most of the land use land cover classification methods presented in the literature have been conducted using satellite remote sensing images. High-resolution aerial imagery is now being used for land cover classification. The Global Learning and Observations to Benefit, the Environment land cover image database, is created by citizen scientists worldwide who use their handheld cameras to take a set of six images per land cover site. These images have clutter due to man-made objects, and the pixel uncertainties result in incorrect labels. The problem of accurate labeling of these land cover images is addressed. An integrated architecture that combines Unet and DeepLabV3 for initial segmentation, followed by a weighted fusion model that combines the segmentation labels, is presented. The land cover images with labels are used for training the deep learning models. The fusion model combines the labels of five images taken from the north, south, east, west, and down directions to assign a unique label to the image sets. 2916 GLOBE images have been labeled with land cover classes using the integrated model with minimal human-in-the-loop annotation. The validation step shows that our architecture of labeling the images results in 90.97% label accuracy. Our fusion model can be used for labeling large databases of land cover classes from RGB images. MDPI 2022-09-13 /pmc/articles/PMC9503776/ /pubmed/36146242 http://dx.doi.org/10.3390/s22186895 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Manzanarez, Sergio Manian, Vidya Santos, Marvin Land Use Land Cover Labeling of GLOBE Images Using a Deep Learning Fusion Model |
title | Land Use Land Cover Labeling of GLOBE Images Using a Deep Learning Fusion Model |
title_full | Land Use Land Cover Labeling of GLOBE Images Using a Deep Learning Fusion Model |
title_fullStr | Land Use Land Cover Labeling of GLOBE Images Using a Deep Learning Fusion Model |
title_full_unstemmed | Land Use Land Cover Labeling of GLOBE Images Using a Deep Learning Fusion Model |
title_short | Land Use Land Cover Labeling of GLOBE Images Using a Deep Learning Fusion Model |
title_sort | land use land cover labeling of globe images using a deep learning fusion model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503776/ https://www.ncbi.nlm.nih.gov/pubmed/36146242 http://dx.doi.org/10.3390/s22186895 |
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