Evaluation of Cloud Mask Performance of KOMPSAT-3 Top-of-Atmosphere Reflectance Incorporating Deeplabv3+ with Resnet 101 Model
Cloud detection is a crucial task in satellite remote sensing, influencing applications such as vegetation indices, land use analysis, and renewable energy estimation. This study evaluates the performance of cloud masks generated for KOMPSAT-3 and KOMPSAT-3A imagery using the DeepLabV3+ deep learnin...
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MDPI AG
2025-04-01
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| author | Suhwan Kim Doehee Han Yejin Lee Eunsu Doo Han Oh Jonghan Ko Jongmin Yeom |
| author_facet | Suhwan Kim Doehee Han Yejin Lee Eunsu Doo Han Oh Jonghan Ko Jongmin Yeom |
| author_sort | Suhwan Kim |
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| description | Cloud detection is a crucial task in satellite remote sensing, influencing applications such as vegetation indices, land use analysis, and renewable energy estimation. This study evaluates the performance of cloud masks generated for KOMPSAT-3 and KOMPSAT-3A imagery using the DeepLabV3+ deep learning model with a ResNet-101 backbone. To overcome the limitations of digital number (DN) data, Top-of-Atmosphere (TOA) reflectance was computed and used for model training. Comparative analysis between the DN and TOA reflectance demonstrated significant improvements with the TOA correction applied. The TOA reflectance combined with the NDVI channel achieved the highest precision (69.33%) and F1-score (59.27%), along with a mean Intersection over Union (mIoU) of 46.5%, outperforming all the other configurations. In particular, this combination was highly effective in detecting dense clouds, achieving an mIoU of 48.12%, while the Near-Infrared, green, and red (NGR) combination performed best in identifying cloud shadows with an mIoU of 23.32%. These findings highlight the critical role of radiometric correction and optimal channel selection in enhancing deep learning-based cloud detection. This study demonstrates the crucial role of radiometric correction, optimal channel selection, and the integration of additional synthetic indices in enhancing deep learning-based cloud detection performance, providing a foundation for the development of more refined cloud masking techniques in the future. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-30d5a13d8a564ae184c17c3f5cae91642025-08-20T02:17:14ZengMDPI AGApplied Sciences2076-34172025-04-01158433910.3390/app15084339Evaluation of Cloud Mask Performance of KOMPSAT-3 Top-of-Atmosphere Reflectance Incorporating Deeplabv3+ with Resnet 101 ModelSuhwan Kim0Doehee Han1Yejin Lee2Eunsu Doo3Han Oh4Jonghan Ko5Jongmin Yeom6Department of Earth and Environmental Sciences, Jeonbuk National University, Jeonju 54896, Republic of KoreaDepartment of Earth and Environmental Sciences, Jeonbuk National University, Jeonju 54896, Republic of KoreaDepartment of Earth and Environmental Sciences, Jeonbuk National University, Jeonju 54896, Republic of KoreaDepartment of Earth and Environmental Sciences, Jeonbuk National University, Jeonju 54896, Republic of KoreaKorea Aerospace Research Institute, 169-84, Gwahak-ro, Yuseong, Daejeon 34133, Republic of KoreaApplied Plant Science, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of KoreaDepartment of Earth and Environmental Sciences, Jeonbuk National University, Jeonju 54896, Republic of KoreaCloud detection is a crucial task in satellite remote sensing, influencing applications such as vegetation indices, land use analysis, and renewable energy estimation. This study evaluates the performance of cloud masks generated for KOMPSAT-3 and KOMPSAT-3A imagery using the DeepLabV3+ deep learning model with a ResNet-101 backbone. To overcome the limitations of digital number (DN) data, Top-of-Atmosphere (TOA) reflectance was computed and used for model training. Comparative analysis between the DN and TOA reflectance demonstrated significant improvements with the TOA correction applied. The TOA reflectance combined with the NDVI channel achieved the highest precision (69.33%) and F1-score (59.27%), along with a mean Intersection over Union (mIoU) of 46.5%, outperforming all the other configurations. In particular, this combination was highly effective in detecting dense clouds, achieving an mIoU of 48.12%, while the Near-Infrared, green, and red (NGR) combination performed best in identifying cloud shadows with an mIoU of 23.32%. These findings highlight the critical role of radiometric correction and optimal channel selection in enhancing deep learning-based cloud detection. This study demonstrates the crucial role of radiometric correction, optimal channel selection, and the integration of additional synthetic indices in enhancing deep learning-based cloud detection performance, providing a foundation for the development of more refined cloud masking techniques in the future.https://www.mdpi.com/2076-3417/15/8/4339operational systemGK-2A satellitedeep learningsolar radiationaerosol optical depth |
| spellingShingle | Suhwan Kim Doehee Han Yejin Lee Eunsu Doo Han Oh Jonghan Ko Jongmin Yeom Evaluation of Cloud Mask Performance of KOMPSAT-3 Top-of-Atmosphere Reflectance Incorporating Deeplabv3+ with Resnet 101 Model Applied Sciences operational system GK-2A satellite deep learning solar radiation aerosol optical depth |
| title | Evaluation of Cloud Mask Performance of KOMPSAT-3 Top-of-Atmosphere Reflectance Incorporating Deeplabv3+ with Resnet 101 Model |
| title_full | Evaluation of Cloud Mask Performance of KOMPSAT-3 Top-of-Atmosphere Reflectance Incorporating Deeplabv3+ with Resnet 101 Model |
| title_fullStr | Evaluation of Cloud Mask Performance of KOMPSAT-3 Top-of-Atmosphere Reflectance Incorporating Deeplabv3+ with Resnet 101 Model |
| title_full_unstemmed | Evaluation of Cloud Mask Performance of KOMPSAT-3 Top-of-Atmosphere Reflectance Incorporating Deeplabv3+ with Resnet 101 Model |
| title_short | Evaluation of Cloud Mask Performance of KOMPSAT-3 Top-of-Atmosphere Reflectance Incorporating Deeplabv3+ with Resnet 101 Model |
| title_sort | evaluation of cloud mask performance of kompsat 3 top of atmosphere reflectance incorporating deeplabv3 with resnet 101 model |
| topic | operational system GK-2A satellite deep learning solar radiation aerosol optical depth |
| url | https://www.mdpi.com/2076-3417/15/8/4339 |
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