A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Images
Accurate detection of clouds and shadows present in optical imagery is important in remote sensing for ensuring data quality and reliability. This study introduces a deep learning model capable of generating precise cloud and shadows masks for subsequent filtering. Unlike other works in literature,...
- Autores:
-
Arrechea-Castillo, Darwin Alexis
Solano-Correa, Yady Tatiana
Muñoz-Ordoñez, Julián Fernando
Pencue-Fierro, Edgar Leonairo
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12731
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12731
- Palabra clave:
- Cloud Detection
Cloud Shadow Detection
Deep Learning
Remote Sensing
MultiSensor
LEMB
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Images |
| title |
A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Images |
| spellingShingle |
A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Images Cloud Detection Cloud Shadow Detection Deep Learning Remote Sensing MultiSensor LEMB |
| title_short |
A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Images |
| title_full |
A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Images |
| title_fullStr |
A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Images |
| title_full_unstemmed |
A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Images |
| title_sort |
A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Images |
| dc.creator.fl_str_mv |
Arrechea-Castillo, Darwin Alexis Solano-Correa, Yady Tatiana Muñoz-Ordoñez, Julián Fernando Pencue-Fierro, Edgar Leonairo |
| dc.contributor.author.none.fl_str_mv |
Arrechea-Castillo, Darwin Alexis Solano-Correa, Yady Tatiana Muñoz-Ordoñez, Julián Fernando Pencue-Fierro, Edgar Leonairo |
| dc.subject.keywords.spa.fl_str_mv |
Cloud Detection Cloud Shadow Detection Deep Learning Remote Sensing MultiSensor |
| topic |
Cloud Detection Cloud Shadow Detection Deep Learning Remote Sensing MultiSensor LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
Accurate detection of clouds and shadows present in optical imagery is important in remote sensing for ensuring data quality and reliability. This study introduces a deep learning model capable of generating precise cloud and shadows masks for subsequent filtering. Unlike other works in literature, this model operates efficiently across diverse temporalities, sensors, and spatial resolutions, without the need for any relative or absolute transformation of the original data. This versatility, to date unreported in the literature, marks a significant advancement in the field. The model utilizes data from PlanetScope, Landsat and Sentinel-2 sensors and is based on a simplified convolutional neural network (CNN) architecture, LeNet, which facilitates easy training on standard computers with minimal time requirements. Despite its simplicity, the model demonstrates robustness, achieving accuracy metrics over 96% in validation data. These results show the model potential in transforming cloud and shadow detection in remote sensing, combining ease of use with high accuracy. |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-09-12T14:01:02Z |
| dc.date.available.none.fl_str_mv |
2024-09-12T14:01:02Z |
| dc.date.issued.none.fl_str_mv |
2024-07-12 |
| dc.date.submitted.none.fl_str_mv |
2024-09-11 |
| dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_8544 |
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info:eu-repo/semantics/lecture |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/resource_type/c_c94f |
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publishedVersion |
| dc.identifier.citation.spa.fl_str_mv |
D.A. Arrechea-Castillo; Y. T. Solano-Correa; J.F. Muñoz-Ordóñez; E. L. Pencue-Fierro, "A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Images," in 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Athens, Greece, Jul. 2024. DOI: 10.1109/IGARSS53475.2024.10640766. |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/12731 |
| dc.identifier.doi.none.fl_str_mv |
10.1109/IGARSS53475.2024.10640766 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
D.A. Arrechea-Castillo; Y. T. Solano-Correa; J.F. Muñoz-Ordóñez; E. L. Pencue-Fierro, "A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Images," in 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Athens, Greece, Jul. 2024. DOI: 10.1109/IGARSS53475.2024.10640766. 10.1109/IGARSS53475.2024.10640766 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/12731 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_14cb |
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info:eu-repo/semantics/closedAccess |
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closedAccess |
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http://purl.org/coar/access_right/c_14cb |
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4 páginas |
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application/pdf |
| dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
| dc.publisher.faculty.spa.fl_str_mv |
Ciencias Básicas |
| dc.source.spa.fl_str_mv |
IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |
| institution |
Universidad Tecnológica de Bolívar |
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Arrechea-Castillo, Darwin Alexis93ac81fe-17eb-4bd1-a2fc-f39f13cc9af2Solano-Correa, Yady Tatiana64432ee7-11fa-4bfb-b643-143125ef82c1Muñoz-Ordoñez, Julián Fernandoe41bd21d-9782-40e9-8351-aeeaa10fa1adPencue-Fierro, Edgar Leonairo6964c8f9-622b-4193-9015-e2dbfaf051272024-09-12T14:01:02Z2024-09-12T14:01:02Z2024-07-122024-09-11D.A. Arrechea-Castillo; Y. T. Solano-Correa; J.F. Muñoz-Ordóñez; E. L. Pencue-Fierro, "A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Images," in 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Athens, Greece, Jul. 2024. DOI: 10.1109/IGARSS53475.2024.10640766.https://hdl.handle.net/20.500.12585/1273110.1109/IGARSS53475.2024.10640766Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarAccurate detection of clouds and shadows present in optical imagery is important in remote sensing for ensuring data quality and reliability. This study introduces a deep learning model capable of generating precise cloud and shadows masks for subsequent filtering. Unlike other works in literature, this model operates efficiently across diverse temporalities, sensors, and spatial resolutions, without the need for any relative or absolute transformation of the original data. This versatility, to date unreported in the literature, marks a significant advancement in the field. The model utilizes data from PlanetScope, Landsat and Sentinel-2 sensors and is based on a simplified convolutional neural network (CNN) architecture, LeNet, which facilitates easy training on standard computers with minimal time requirements. Despite its simplicity, the model demonstrates robustness, achieving accuracy metrics over 96% in validation data. These results show the model potential in transforming cloud and shadow detection in remote sensing, combining ease of use with high accuracy.4 páginasapplication/pdfengIEEE International Geoscience and Remote Sensing Symposium (IGARSS)A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Imagesinfo:eu-repo/semantics/lectureinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_c94fhttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_8544Cloud DetectionCloud Shadow DetectionDeep LearningRemote SensingMultiSensorLEMBinfo:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbCartagena de IndiasCiencias BásicasInvestigadoresZ. Li, H. Shen, Q. Weng, Y. Zhang, P. Dou, and L. Zhang, “Cloud and cloud shadow detection for optical satellite imagery: Features, algorithms, validation, and prospects,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 188, pp. 89–108, 2022.R. Gupta and S. J. Nanda, “Cloud detection in satellite images with classical and deep neural network approach: A review,” Multimedia Tools and Applications, vol. 81, no. 22, pp. 31847–31880, 2022.A. Francis, P. Sidiropoulos, and J.-P. Muller, “Cloud- FCN: Accurate and Robust Cloud Detection for Satellite Imagery with Deep Learning,” Remote Sensing, vol. 11, no. 19, p. 2312, 2019.M. Khoshboresh-Masouleh and R. Shah-Hosseini, “A Deep Learning Method for Near-Real-Time Cloud and Cloud Shadow Segmentation from Gaofen-1 Images,” Computational Intelligence and Neuroscience, vol. 2020, p. e8811630, 2020.K. Xu, K. Guan, J. Peng, Y. Luo, and S. Wang, “Deep- Mask: An algorithm for cloud and cloud shadow detection in optical satellite remote sensing images using deep residual network,” 2019.H. Zhai, H. Zhang, L. Zhang, and P. Li, “Cloud/shadow detection based on spectral indices for multi/hyperspectral optical remote sensing imagery,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 144, pp. 235–253, 2018.D. P. Roy, H. Huang, R. Houborg, and V. S. Martins, “A global analysis of the temporal availability of PlanetScope high spatial resolution multi-spectral imagery,” Remote Sensing of Environment, vol. 264, no. 112586, p. 21, 2021.Z. Li, H. Shen, Q. Cheng, Y. Liu, S. You, and Z. He, “Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors,” Isprs Journal of Photogrammetry and Remote Sensing, vol. 150, pp. 197–212, 2019.S. Mahajan and B. Fataniya, “Cloud detection methodologies: Variants and development—a review,” Complex & Intelligent Systems, vol. 6, no. 2, pp. 251–261, 2020.N. Ma, L. Sun, C. Zhou, and Y. He, “Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network,” REMOTE SENSING, vol. 13, no. 16, p. 3319, 2021.D. Montero, C. Aybar, M. D. Mahecha, F. Martinuzzi, M. S¨ochting, and S.Wieneke, “A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research,” Scientific Data, vol. 10, no. 1, p. 197, 2023.X. Xiang, K. Li, B. Huang, and Y. Cao, “A Multi- Sensor Data-Fusion Method Based on Cloud Model and Improved Evidence Theory,” Sensors, vol. 22, no. 15, p. 5902, 2022.PLANET.COM, “Planet Imagery Product Specifications,” 2022.D. A. Arrechea-Castillo, Y. T. Solano-Correa, J. F. Mu˜noz-Ord´o˜nez, E. L. Pencue-Fierro, and A. Figueroa- Casas, “Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning,” Remote Sensing, vol. 15, no. 10, p. 2521, 2023.http://purl.org/coar/resource_type/c_c94fORIGINAL2024-C-A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Images.pdf2024-C-A Deep Learning Approach To Cloud And Shadow Detection In Multiresolution, Multitemporal And Multisensor Images.pdfapplication/pdf2769075https://repositorio.utb.edu.co/bitstream/20.500.12585/12731/1/2024-C-A%20Deep%20Learning%20Approach%20To%20Cloud%20And%20Shadow%20Detection%20In%20Multiresolution%2c%20Multitemporal%20And%20Multisensor%20Images.pdf6bef9e581ad51e62fe66410dba224f76MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/12731/2/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD52TEXT2024-C-A Deep Learning Approach To Cloud And Shadow Detection In 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