SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing  
[PERSON]  
1 Engineering Faculty, San Juan del Rio Campus, Autonomous University of Queretaro, Rio Moctezuma 249, San Juan del Rio 76807, Mexico; [EMAIL_ADDRESS] (G.A.-R.); [EMAIL_ADDRESS] (I.A.E.-V.); [EMAIL_ADDRESS] (C.G.M.-P.)  
1 Engineering Faculty, San Juan del Rio Campus, Autonomous University of Queretaro, Rio Moctezuma 249, San Juan del Rio 76807, Mexico; [EMAIL_ADDRESS] (G.A.-R.); [EMAIL_ADDRESS] (I.A.E.-V.); [EMAIL_ADDRESS] (C.G.M.-P.)  
2C.A. making single-image shadow detection increasingly important [11; 12; 13]. To address these challenges, this research proposes a novel SDKU-Net architecture that generalizes complex shadow features within images through a performance-based dynamic kernel-based architecture and aims to improve shadow detection performance, particularly in urban environments where shadow effects are more intricate and impactful. Shadow detection has become a widely studied topic in recent years, focusing on both shadow properties and geometric characteristics. Property-based methods leverage specific attributes extracted from images, including spatial characteristics such as texture, shape, and boundaries [14; 15], as well as color characteristics [16; 17], often employing thresholding techniques. For instance, ref. [18] introduced a methodology that enhances shadows using a shadow detection index (SDI) combined with threshold segmentation to identify shadow masks, demonstrating the utility of shadow-related color information. Despite these advancements, challenges remain, particularly in differentiating between shadows and dark-colored objects. The complexity of remote sensing imagery further exacerbates the correlation between adjacent objects [19]. These issues highlight the need for more robust and context-aware methodologies to improve shadow detection in diverse and intricate environments. In recent years, binary shadow detection tasks have increasingly been tackled using machine learning-based methods. Some of these methods employ learning-based classifiers, such as Random Forest [20], K-Nearest Neighbors (KNN) [21], and Support Vector Machines (SVMs) [22], to perform shadow segmentation in input images. These approaches typically rely on low-level visual features such as color and texture. Although these methods can yield accurate results for relatively simple scenes, their main limitation lies in their reliance on high-quality, diverse labeled datasets. Additionally, they often struggle with handling dynamic lighting conditions. Furthermore, many techniques face scalability issues, overfitting, and challenges in generalizing to new scenarios, particularly when applied to complex images, such as those obtained through remote sensing. To address certain limitations, deep learning techniques, such as convolutional neural networks (CNNs), have emerged as powerful tools for image processing. CNNs leverage multiple convolutional layers to automatically learn and extract complex, non-linear features from training data. By utilizing hierarchical feature representations, CNN-based methods have demonstrated superior performance in shadow detection, particularly in scenarios with high variability and complex image contexts [23; 24; 25]. However, generalization remains challenging, especially for intricate features in local regions. To tackle this issue, several novel approaches have been proposed, including the Context-based Feature Enhancement Module (CFEM) [26], the combination of CNNs with Shunted Transformers [27], and the Cross-Attentional Dual-Decoder Network (CADDN) [28]. Building on CNNs, U-Net introduced a key structural innovation: its symmetric encoder-decoder architecture with skip connections [29], enabling the network to capture both global shadow characteristics (such as overall shape and distribution) and local shadow details (such as discontinuities and fine boundaries) [30; 31; 32].