Optimized Ensemble Learning for Semantic Segmentation of Satellite Imagery Using DeepLabV3+ and UNet With PSO and Cross-Dataset Evaluation

Semantic segmentation is critical in remote sensing applications such as urban planning, disaster management, and environmental monitoring. However, segmenting complex satellite images remains challenging due to varied textures and overlapping boundaries. This study proposes a PSO-optimized ensemble framework integrating DeepLabV3+ and UNet to enhance segmentation precision and reliability. DeepLabV3+ captures global contextual information, while UNet preserves fine spatial details, enabling the ensemble to achieve superior segmentation outcomes. The model is trained using the Semantic Segmentation of Aerial Imagery dataset and optimized with Particle Swarm Optimization (PSO) for adaptive learning rate tuning, ensuring convergence stability and performance maximization. Experimental evaluations demonstrate the ensemble’s ability to outperform individual models, achieving a Dice score of 0.9201 compared to DeepLabV3+ (0.8538) and UNet (0.7877). Further, cross-dataset validation on the Bhuvan Satellite Data confirms its generalization capability with an unprecedented Dice score of 0.9999, indicating robust segmentation across diverse landscapes. These findings highlight the effectiveness of PSO-driven ensemble learning for high-precision satellite image analysis, offering a scalable solution for real-world geospatial applications. Our approach sets a new benchmark in segmentation accuracy and cross-domain adaptability, enhancing the practical utility of semantic segmentation in remote sensing tasks. In this context, PSO is introduced as a metaheuristic strategy to optimize the learning rate of the ensemble model adaptively. Unlike static or heuristically chosen learning rates, PSO dynamically explores the search space based on segmentation loss feedback, enabling improved convergence and reduced training instability. This adaptive mechanism is particularly valuable in cross-dataset semantic segmentation tasks, where variability in resolution, scene complexity, and geographic distribution demands robust generalization and precise learning rate adjustment.