Accurate high-resolution information on seabed sediment distribution is important for marine engineering investigations, submarine pipeline planning, and cross-sea passage assessment. However, multibeam seabed sediment classification is often constrained by the limited number and spatial representativeness of field samples, as well as ambiguous acoustic responses in heterogeneous seabed environments. This study presents a classification framework integrating multibeam backscatter features, topographic features, Linear Spectral Clustering (LSC) superpixel segmentation sample augmentation, and grey wolf optimizer-enhanced XGBoost (GWO-XGBoost), and applies it to the proposed Bohai Strait cross-sea passage area. Nine backscatter texture features and seven topographic features were extracted from 5 m resolution bathymetric and backscatter data acquired using a SeaBat T50-R multibeam system. LSC-based augmentation generated 12,061 pixel-level samples from 52 original grab locations. To prevent information leakage between training and validation data, 51 of the 52 original grab locations were assigned to five folds before augmentation, with all pixels derived from the same grab kept within the corresponding fold; the single sM grab was retained as training support. Under this grab-grouped validation framework, LSC combined with GWO-XGBoost achieved a mean overall accuracy of 72.38 ± 6.33% and a mean Kappa coefficient of 0.6353 ± 0.0877 at the augmented-pixel level. A complementary grab-level evaluation, in which each validation-eligible grab contributed one prediction at its original coordinate, yielded a mean overall accuracy of 58.91 ± 10.29% and a mean Kappa coefficient of 0.4850 ± 0.1341. The classification map indicates that silty sand and slightly gravelly muddy sand are the most widely mapped sediment types in the study area. Gravel-bearing sediments were more frequently mapped in areas associated with relatively steep slopes and channel- or trough-like topography, whereas sandy silt and silty sand were more widely distributed in relatively gentle areas. The results indicate the potential of the integrated LSC–GWO-XGBoost workflow for regional seabed sediment characterization under limited field sampling. Nevertheless, the augmented pixels were derived from only 52 original grab locations, of which 51 were independently evaluated, and therefore do not constitute an equivalent number of independent sediment observations. The classification map should consequently be interpreted as preliminary regional-scale sediment information rather than design-stage engineering ground truth.

Wetland landscape classification is fundamental to monitoring changes in ecosystem patterns. This study proposes an ensemble classification approach that integrates Maximum Likelihood Classification (MLC) and Decision Tree (DT) methods with optimized feature selection for long-term wetland monitoring in the Liaohe Delta, China. Based on four periods of Landsat remote sensing images from 1987, 1997, 2007, and 2017, multi-dimensional features including PCA1, TCT, NDVI, and MNDWI were extracted to construct a hierarchical classification system comprising 13 landscape types. The results show that the integrated method achieved an overall classification accuracy of 87.71% and a Kappa coefficient of 0.85, improving by 16.50% and 19.72%, respectively, compared with the single MLC approach. The classification results reveal significant spatiotemporal variations in landscape patterns. Reed wetlands decreased from 1284.44 km² in 1987 to 1006.70 km² in 1997, followed by a recovery to 1275.53 km² in 2017. In contrast, Suaeda communities experienced severe degradation, declining sharply from 227.48 km² in 1987 to 30.52 km² in 2017. Meanwhile, the coastline advanced landward by 263.24 km², with the proportion of artificial shoreline increasing from 12.4% to 38.7%. The changes in wetland landscape types in the Liaohe Delta from 1987 to 2017 were mainly influenced by urbanization processes and ecological restoration policies. These findings indicate that the proposed method can effectively support long-term wetland landscape dynamics analysis and provide a useful reference for coastal wetland management.