Optimizing the Hyperparameters of Fully Convolutional Encoder–Decoder Networks for SAR Image Segmentation

Fully convolutional encoder-decoder networks have been developed for the segmentation of sensing synthetic aperture radar (SAR) images. A recent one called the multiscaled attention U-net with dilated convolution and offset convolution (MDOAU-net) has been proposed for SAR image segmentation in aquaculture raft monitoring. Despite its excellent performance, its hyperparameters have to be handcrafted based on human experience, consuming a significant amount of time to tune. In this letter, a swarm intelligence algorithm is leveraged to optimize the hyperparameters of fully convolutional encoder-decoder networks (particularly MDOAU-net), including their kernel size, dilation rate, learning rate, batch size, and activation function indicator. Based on segmentation performance, early-stop termination criteria are introduced into a particle swarm optimization (PSO) algorithm to avoid overusing computing resources to train the networks. Specifically, the hyperparameters are optimized using the PSO algorithm with early-stop termination criteria. Experimental results show that the segmentation accuracy of the proposed method reaches 91.49%, which statistically outperforms other methods.