Uncertainty Estimation in Bayesian Convolutional Neural Networks for SAR Ship Classification

This study introduces Bayesian Convolutional Neural Networks (BCNNs) for SAR ship classification, comparing their performance with traditional CNNs using the LeNet-5 architecture and VGG16 as a benchmark. It highlights BCNNs’ capability in uncertainty decomposition and improved model accuracy, demonstrated using the OpenSARShip dataset. Though further investigations using more complex datasets and additional network designs are necessary, the presented work highlights the potential of BCNNs in producing more reliable predictions in SAR ship classification.