The acquisition of high-resolution (HR) digital bathymetric models (DBMs) is crucial for oceanic research activities. However, obtaining HR DBM data is challenging, which has led to the use of super-resolution (SR) methods to improve the DBM’s resolution, as, unfortunately, existing interpolation methods for DBMs suffer from low precision, which limits their practicality. To address this issue, we propose a seabed terrain feature extraction transform model that combines the seabed terrain feature extraction module with the efficient transform module, focusing on the terrain characteristics of DBMs. By taking advantage of these two modules, we improved the efficient extraction of seabed terrain features both locally and globally, and as a result, we obtained a highly accurate SR reconstruction of DBM data within the study area, including the Mariana Trench in the Pacific Ocean and the adjacent sea. A comparative analysis with bicubic interpolation, SRCNN, SRGAN, and SRResNet shows that the proposed method decreases the root mean square error (RMSE) by 16%, 10%, 13%, and 12%, respectively. These experimental results confirm the high accuracy of the proposed method in terms of reconstructing HR DBMs.

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has excellent potential for obtaining water depth information around islands and reefs. Combining the density-based spatial clustering of applications with noise algorithm (DBSCAN) and multiscale quadtree analysis, we propose a new photon-counting lidar denoising method to discard the large amount of noise in ICESat-2 data. First, the kernel density estimation (KDE) is used to preprocess the point cloud data, and a threshold is set to remove the noise photons on the sea surface. Next, the DBSCAN algorithm is used to preliminarily remove underwater noise photons. Then, the quadtree segmentation and Otsu algorithm are used for fine denoising to extract accurate bottom signal photons. Based on ICESat-2 pho-ton-counting data from six typical islands and reefs worldwide, the proposed method outperforms other algorithms in terms of denoising effect. Compared to in situ data, the determination coefficient (R²) reaches 94.59%, and the root mean square error (RMSE) is 1.01 m. The proposed method can extract accurate underwater terrain information, laying a foundation for offshore bathymetry.

The Wide Field of View (WFV) imaging system equipped on the GaoFen-6 (GF-6) optical remote sensing satellite can acquire an image with a swath width 800 km and a resolution of 16 m, which is the largest Earth observation swath width among similar satellites in the world. With the advantages of the high spatial resolution and the wide field of view, GF-6 WFV images are widely used in agricultural resources monitoring, forestry resources investigation, and disaster relief. However, the existence of clouds is inevitable problem in GF-6 WFV images, which influences their availability. To quickly and accurately detect cloud areas in GF-6 WFV images, a cloud detection method for GF-6 WFV images based on the spectrum and variance of superpixels is proposed in the paper. First, the GF-6 WFV image is down-sampled. The simple linear iterative clustering algorithm is used to segment down-sampled images to obtain superpixels. The initial cloud detection result is obtained based on the spectrum of superpixels. Second, the initial cloud detection result is refined based on the variance of superpixels to eliminate the influence of cloud-like ground objects. Finally, the refined cloud detection result is post-processed using the region growing algorithm and expansion algorithm. The post-processed cloud detection result is up-sampled to obtain cloud detection result of the GF-6 WFV image. The experimental results show that the recall and precision of the proposed method are 84.61% and 88.46%, respectively, providing good cloud detection results for GF-6 WFV images.

Tidal creeks are vital geomorphological features of tidal flats, and their spatial and temporal variations contribute significantly to the preservation of ecological diversity and the spatial evolution of coastal wetlands. Traditional methods, such as manual annotation and machine learning, remain common for tidal creek extraction, but they are slow and inefficient. With increasing data volumes, accurately analyzing tidal creeks over large spatial and temporal scales has become a significant challenge. This study proposes a residual U-Net model that utilizes full-dimensional dynamic convolution to segment tidal creeks in the Yellow River Delta, employing Gaofen-2 satellite images with a resolution of 4 m. The model replaces the traditional convolutions in the residual blocks of the encoder with Omni-dimensional Dynamic Convolution (ODConv), mitigating the loss of fine details and improving segmentation for small targets. Adding coordinate attention (CA) to the Atrous Spatial Pyramid Pooling (ASPP) module improves target classification and localization in remote sensing images. Including dice coefficients in the focal loss function improves the model’s gradient and tackles class imbalance within the dataset. Furthermore, the inclusion of dice coefficients in the focal loss function improves the gradient of the model and tackles the dataset’s class inequality. The study results indicate that the model attains an F1 score and kappa coefficient exceeding 80% for both mud and salt marsh regions. Comparisons with several semantic segmentation models on the mud marsh tidal creek dataset show that ODU-Net significantly enhances tidal creek segmentation, resolves class imbalance issues, and delivers superior extraction accuracy and stability.

Substrate classification based on remote sensing images can provide fundamental data for offshore engineering construction and coastal ecological protection without in situ data. To address the inadequate consideration of features in existing substrate classification methods and to mitigate the low classification accuracy caused by noise in single-temporal images, this paper improves the existing remote sensing classification methods and proposes a shallow-water substrate classification method based on the fusion of multitemporal remote sensing images using a random forest model. The proposed method proposes and applies adaptive weighted fusion to bathymetric inversion results from single-temporal images for optimal water depth and terrain features. It derives and constructs a depth-independent bottom reflectance equation, uses median fusion to generate optimal images for accurate bottom reflectance and spectral features, then feeds selected features into the random forest model for training to produce final classification results. Three different types of shallow-water areas were selected for the experiments. The results show that the proposed method effectively leverages multiple features and capitalizes on multitemporal advantages to circumvent the limitations of single-temporal classification, the classification accuracy reaches over 90.34% on different substrates, the maximum misclassification rate is below 8.13% and the number of misclassified pixels is significantly less than that of single-temporal images. Compared with the four mainstream classification algorithms, the proposed method not only achieves overall accuracy (OA) and F1-score exceeding 0.94 and a kappa coefficient greater than 0.90, but demonstrates excellent detail preservation capability and strong adaptability to complex shallow-water environments, and delivers stable classification results.