SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing
Shadows in remote sensing images often introduce challenges in accurate segmentation due to their variability in shape, size, and texture. To address these issues, this study proposes the Supervised Dynamic Kernel U-Net (SDKU-Net), a novel architecture designed to enhance shadow detection in complex...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/3/80 |
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| author | Gilberto Alvarado-Robles Isac Andres Espinosa-Vizcaino Carlos Gustavo Manriquez-Padilla Juan Jose Saucedo-Dorantes |
| author_facet | Gilberto Alvarado-Robles Isac Andres Espinosa-Vizcaino Carlos Gustavo Manriquez-Padilla Juan Jose Saucedo-Dorantes |
| author_sort | Gilberto Alvarado-Robles |
| collection | DOAJ |
| description | Shadows in remote sensing images often introduce challenges in accurate segmentation due to their variability in shape, size, and texture. To address these issues, this study proposes the Supervised Dynamic Kernel U-Net (SDKU-Net), a novel architecture designed to enhance shadow detection in complex remote sensing scenarios. SDKU-Net integrates dynamic kernel adjustment, a combined loss function incorporating Focal and Tversky Loss, and optimizer switching to effectively tackle class imbalance and improve segmentation quality. Using the AISD dataset, the proposed method achieved state-of-the-art performance with an Intersection over Union (IoU) of 0.8552, an F1-Score of 0.9219, an Overall Accuracy (OA) of 96.50%, and a Balanced Error Rate (BER) of 5.08%. Comparative analyses demonstrate SDKU-Net’s superior performance against established methods such as U-Net, U-Net++, MSASDNet, and CADDN. Additionally, the model’s efficient training process, requiring only 75 epochs, highlights its potential for resource-constrained applications. These results underscore the robustness and adaptability of SDKU-Net, paving the way for advancements in shadow detection and segmentation across diverse fields. |
| format | Article |
| id | doaj-art-6fba2fd8a1724638bb5274c62caa2269 |
| institution | OA Journals |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-6fba2fd8a1724638bb5274c62caa22692025-08-20T02:11:18ZengMDPI AGComputers2073-431X2025-02-011438010.3390/computers14030080SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote SensingGilberto Alvarado-Robles0Isac Andres Espinosa-Vizcaino1Carlos Gustavo Manriquez-Padilla2Juan Jose Saucedo-Dorantes3Engineering Faculty, San Juan del Rio Campus, Autonomous University of Queretaro, Rio Moctezuma 249, San Juan del Rio 76807, MexicoEngineering Faculty, San Juan del Rio Campus, Autonomous University of Queretaro, Rio Moctezuma 249, San Juan del Rio 76807, MexicoEngineering Faculty, San Juan del Rio Campus, Autonomous University of Queretaro, Rio Moctezuma 249, San Juan del Rio 76807, MexicoEngineering Faculty, San Juan del Rio Campus, Autonomous University of Queretaro, Rio Moctezuma 249, San Juan del Rio 76807, MexicoShadows in remote sensing images often introduce challenges in accurate segmentation due to their variability in shape, size, and texture. To address these issues, this study proposes the Supervised Dynamic Kernel U-Net (SDKU-Net), a novel architecture designed to enhance shadow detection in complex remote sensing scenarios. SDKU-Net integrates dynamic kernel adjustment, a combined loss function incorporating Focal and Tversky Loss, and optimizer switching to effectively tackle class imbalance and improve segmentation quality. Using the AISD dataset, the proposed method achieved state-of-the-art performance with an Intersection over Union (IoU) of 0.8552, an F1-Score of 0.9219, an Overall Accuracy (OA) of 96.50%, and a Balanced Error Rate (BER) of 5.08%. Comparative analyses demonstrate SDKU-Net’s superior performance against established methods such as U-Net, U-Net++, MSASDNet, and CADDN. Additionally, the model’s efficient training process, requiring only 75 epochs, highlights its potential for resource-constrained applications. These results underscore the robustness and adaptability of SDKU-Net, paving the way for advancements in shadow detection and segmentation across diverse fields.https://www.mdpi.com/2073-431X/14/3/80SDKU-Netdynamic kernelsremote sensingshadow detection |
| spellingShingle | Gilberto Alvarado-Robles Isac Andres Espinosa-Vizcaino Carlos Gustavo Manriquez-Padilla Juan Jose Saucedo-Dorantes SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing Computers SDKU-Net dynamic kernels remote sensing shadow detection |
| title | SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing |
| title_full | SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing |
| title_fullStr | SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing |
| title_full_unstemmed | SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing |
| title_short | SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing |
| title_sort | sdku net a novel architecture with dynamic kernels and optimizer switching for enhanced shadow detection in remote sensing |
| topic | SDKU-Net dynamic kernels remote sensing shadow detection |
| url | https://www.mdpi.com/2073-431X/14/3/80 |
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