MambaSegNet: A Fast and Accurate High-Resolution Remote Sensing Imagery Ship Segmentation Network

High-resolution remote sensing imagery is crucial for ship extraction in ocean-related applications. Existing object detection and semantic segmentation methods for ship extraction have limitations: the former cannot precisely obtain ship shapes, while the latter struggles with small targets and complex backgrounds. This study addresses these issues by constructing two datasets, DIOR_SHIP and LEVIR_SHIP, using the SAM model and morphological operations. A novel MambaSegNet is then designed based on the advanced Mamba architecture. It is an encoder–decoder network with MambaLayer and ResMambaBlock for effective multi-scale feature processing. The experiments conducted with seven mainstream models show that the IOU of MambaSegNet is 0.8208, the Accuracy is 0.9176, the Precision is 0.9276, the Recall is 0.9076, and the F1-score is 0.9176. Compared with other models, it acquired the best performance. This research offers a valuable dataset and a novel model for ship extraction, with potential cross-domain application prospects.