{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T23:48:24Z","timestamp":1772495304827,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T00:00:00Z","timestamp":1740268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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\u2019s superior performance against established methods such as U-Net, U-Net++, MSASDNet, and CADDN. Additionally, the model\u2019s 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.<\/jats:p>","DOI":"10.3390\/computers14030080","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T10:55:48Z","timestamp":1740480948000},"page":"80","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6667-2297","authenticated-orcid":false,"given":"Gilberto","family":"Alvarado-Robles","sequence":"first","affiliation":[{"name":"Engineering Faculty, San Juan del Rio Campus, Autonomous University of Queretaro, Rio Moctezuma 249, San Juan del Rio 76807, Mexico"}]},{"given":"Isac Andres","family":"Espinosa-Vizcaino","sequence":"additional","affiliation":[{"name":"Engineering Faculty, San Juan del Rio Campus, Autonomous University of Queretaro, Rio Moctezuma 249, San Juan del Rio 76807, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1332-5173","authenticated-orcid":false,"given":"Carlos Gustavo","family":"Manriquez-Padilla","sequence":"additional","affiliation":[{"name":"Engineering Faculty, San Juan del Rio Campus, Autonomous University of Queretaro, Rio Moctezuma 249, San Juan del Rio 76807, Mexico"},{"name":"C.A. Mechanical and Automotive Systems Applied to the Management of Conventional and Alternative Energies (UAQ-CA-155), Autonomous University of Queretaro, San Juan del Rio 76806, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9026-6694","authenticated-orcid":false,"given":"Juan Jose","family":"Saucedo-Dorantes","sequence":"additional","affiliation":[{"name":"Engineering Faculty, San Juan del Rio Campus, Autonomous University of Queretaro, Rio Moctezuma 249, San Juan del Rio 76807, Mexico"},{"name":"C.A. Mechanical and Automotive Systems Applied to the Management of Conventional and Alternative Energies (UAQ-CA-155), Autonomous University of Queretaro, San Juan del Rio 76806, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,23]]},"reference":[{"key":"ref_1","first-page":"103514","article-title":"Multi-scale Feature Fusion and Transformer Network for urban green space segmentation from high-resolution remote sensing images","volume":"124","author":"Cheng","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.comcom.2024.01.032","article-title":"Semantic segmentation of deep learning remote sensing images based on band combination principle: Application in urban planning and land use","volume":"217","author":"Jia","year":"2024","journal-title":"Comput. 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