Cargando…
A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery
Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning (DL) algorithms have the ability to extract infor...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8002436/ https://www.ncbi.nlm.nih.gov/pubmed/33809792 http://dx.doi.org/10.3390/s21062089 |
_version_ | 1783671463141703680 |
---|---|
author | Li, Meng Tang, Zhuang Tong, Wei Li, Xianju Chen, Weitao Wang, Lizhe |
author_facet | Li, Meng Tang, Zhuang Tong, Wei Li, Xianju Chen, Weitao Wang, Lizhe |
author_sort | Li, Meng |
collection | PubMed |
description | Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning (DL) algorithms have the ability to extract informative features, they require large amounts of sample data. As a result, the design of more interpretable DL models with lower sample demand is highly important. In this study, a novel multi-level output-based deep belief network (DBN-ML) model was developed based on Ziyuan-3 imagery, which was applied for fine classification in an open-pit mine area of Wuhan City. First, the last DBN layer was used to output fine-scale land cover types. Then, one of the front DBN layers outputted the first-level land cover types. The coarse classification was easier and fewer DBN layers were sufficient. Finally, these two losses were weighted to optimize the DBN-ML model. As the first-level class provided a larger amount of additional sample data with no extra cost, the multi-level output strategy enhanced the robustness of the DBN-ML model. The proposed model produces an overall accuracy of 95.10% and an F1-score of 95.07%, outperforming some other models. |
format | Online Article Text |
id | pubmed-8002436 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80024362021-03-28 A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery Li, Meng Tang, Zhuang Tong, Wei Li, Xianju Chen, Weitao Wang, Lizhe Sensors (Basel) Article Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning (DL) algorithms have the ability to extract informative features, they require large amounts of sample data. As a result, the design of more interpretable DL models with lower sample demand is highly important. In this study, a novel multi-level output-based deep belief network (DBN-ML) model was developed based on Ziyuan-3 imagery, which was applied for fine classification in an open-pit mine area of Wuhan City. First, the last DBN layer was used to output fine-scale land cover types. Then, one of the front DBN layers outputted the first-level land cover types. The coarse classification was easier and fewer DBN layers were sufficient. Finally, these two losses were weighted to optimize the DBN-ML model. As the first-level class provided a larger amount of additional sample data with no extra cost, the multi-level output strategy enhanced the robustness of the DBN-ML model. The proposed model produces an overall accuracy of 95.10% and an F1-score of 95.07%, outperforming some other models. MDPI 2021-03-16 /pmc/articles/PMC8002436/ /pubmed/33809792 http://dx.doi.org/10.3390/s21062089 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Meng Tang, Zhuang Tong, Wei Li, Xianju Chen, Weitao Wang, Lizhe A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery |
title | A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery |
title_full | A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery |
title_fullStr | A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery |
title_full_unstemmed | A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery |
title_short | A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery |
title_sort | multi-level output-based dbn model for fine classification of complex geo-environments area using ziyuan-3 tms imagery |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8002436/ https://www.ncbi.nlm.nih.gov/pubmed/33809792 http://dx.doi.org/10.3390/s21062089 |
work_keys_str_mv | AT limeng amultileveloutputbaseddbnmodelforfineclassificationofcomplexgeoenvironmentsareausingziyuan3tmsimagery AT tangzhuang amultileveloutputbaseddbnmodelforfineclassificationofcomplexgeoenvironmentsareausingziyuan3tmsimagery AT tongwei amultileveloutputbaseddbnmodelforfineclassificationofcomplexgeoenvironmentsareausingziyuan3tmsimagery AT lixianju amultileveloutputbaseddbnmodelforfineclassificationofcomplexgeoenvironmentsareausingziyuan3tmsimagery AT chenweitao amultileveloutputbaseddbnmodelforfineclassificationofcomplexgeoenvironmentsareausingziyuan3tmsimagery AT wanglizhe amultileveloutputbaseddbnmodelforfineclassificationofcomplexgeoenvironmentsareausingziyuan3tmsimagery AT limeng multileveloutputbaseddbnmodelforfineclassificationofcomplexgeoenvironmentsareausingziyuan3tmsimagery AT tangzhuang multileveloutputbaseddbnmodelforfineclassificationofcomplexgeoenvironmentsareausingziyuan3tmsimagery AT tongwei multileveloutputbaseddbnmodelforfineclassificationofcomplexgeoenvironmentsareausingziyuan3tmsimagery AT lixianju multileveloutputbaseddbnmodelforfineclassificationofcomplexgeoenvironmentsareausingziyuan3tmsimagery AT chenweitao multileveloutputbaseddbnmodelforfineclassificationofcomplexgeoenvironmentsareausingziyuan3tmsimagery AT wanglizhe multileveloutputbaseddbnmodelforfineclassificationofcomplexgeoenvironmentsareausingziyuan3tmsimagery |