APCGNet: An All-Pair Correction Graph Network for Ship-Type Recognition with Seabed-Buried Receivers Under Environmental Noise Mismatch

The type and intensity of ocean background noise vary with time and location. Deployed sensors therefore often encounter noise conditions that differ from those represented in the training and validation data for ship-type recognition. When a sensor is buried in the seabed, ship-radiated noise undergoes frequency-selective attenuation and phase distortion as it propagates through sediment, further complicating recognition under this noise mismatch. We propose APCGNet to address this problem. An iterative short-time Fourier transform produces the initial classification representation. Modulation evidence drives an all-pair correction graph, and bounded residual fusion adjusts class scores while limiting the magnitude of each correction. Feature augmentation diversifies the training inputs, and four-model voting combines member predictions at inference. Three independent sets of random seeds were used to train three complete four-member voting systems. On a test set generated by propagating DeepShip recordings through a seabed-burial model, APCGNet achieved the highest mean accuracy over the three runs in all five experiments: baseline noise mismatch, burial-depth variation, incidence-angle variation, unseen grain sizes, and joint mismatch. Its advantage over the runner-up ranged from 0.98 to 2.10 percentage points across these experiments.