Evolutionary Optimization of U-Net Hyperparameters for Enhanced Semantic Segmentation in Remote Sensing Imagery

Remote sensing-based Earth observation provides essential spatial data for analyzing and monitoring both natural and urban environments. Precise characterization of objects in these scenes is vital for environmental management, land-use planning, and monitoring global change. Semantic segmentation of remote sensing imagery (RSI) is a fundamental yet complex task due to significant variability in object shape, scale, and distribution, as well as the complexity of multiscale landscapes captured by advanced sensors. Convolutional neural networks, especially the U-Net architecture, have achieved notable success in segmentation tasks. However, their application in remote sensing is often impeded by persistent issues such as loss of spatial detail, substantial intra- and inter-class variability, and high sensitivity to hyperparameter settings. Manual tuning of hyperparameters is typically inefficient and error-prone, which highlights the importance of heuristic methods for automated optimization. Genetic Algorithms (GAs), Differential Evolution (DE), and Particle Swarm Optimization (PSO) are metaheuristics that provide systematic approaches for exploring large hyperparameter spaces. This study investigates an evolutionary framework for the automated optimization of four critical U-Net hyperparameters—learning rate, number of training epochs, optimizer, and loss function—using micro-evolutionary algorithms. Specifically, micro Genetic Algorithms (micro-GAs), micro Differential Evolution (micro-DE), and micro Particle Swarm Optimization (micro-PSO) are employed to efficiently explore the hyperparameter search space under reduced population settings. The experimental results demonstrate that the proposed micro-evolutionary optimization framework consistently enhances segmentation performance, achieving improvements in Mean Intersection over Union (MIoU) ranging from 3% to 35%, along with systematic gains in overall accuracy across different datasets and configurations.