RGB UAV Imagery Segmentation: A Comparative Study

Semantic segmentation of remote sensing images is vital in computer vision for detailed scene understanding, with applications spanning agriculture, environmental monitoring, and urban planning. In this study, we conduct a comparative analysis of advanced RGB image segmentation methods using various Fully Convolutional Neural Network (FCN) architectures. The architectures evaluated include Attention U-Net, Deeplabv3+, U-Nets with backbones such as ResNetv2, MobileNetv2, VGG19, and EfficientNet. Our experiments on UAV-captured images from the Mpokwe Extension Planning Area (EPA) in Zomba, Malawi, reveal that the ResNetv2-based U-Net achieves the highest Intersection over Union (IoU) score of 0.91. This model effectively segments classes like maize, water, trees, bare land, built-up areas, and other vegetation types, facilitating applications in agricultural yield estimation, land use analysis, and environmental monitoring. The MobileNetv2-based U-Net provides a good balance between performance and efficiency, making it suitable for real-time applications. This work demonstrates the potential of leveraging cost-effective RGB sensors to achieve competitive performance in image segmentation, contributing to the Sustainable Development Goals (SDGs) by informing agricultural practices and environmental stewardship.