Ocean fronts have an important influence on global ocean–atmosphere interactions and marine fishery. Hence, it is of great significance to obtain the positions of the ocean fronts. However, current ocean front detection research confronts two challenges: scarcity of labeled data and limitations of ocean front detection algorithms. To address these two problems, we have collected and labeled an ocean front data set and proposed a new deep learning model for ocean front detection. For concreteness, due to the weak edge property of the ocean fronts, we formulate ocean front detection as a weak edge identification problem and propose the weak edge identification network (WEIN) for ocean front detection. WEIN consists of four convolutional blocks. Each block has a side output layer used to detect front edges at a specific image representation level. The side outputs are then fused to predict (detect) the locations of the ocean fronts. In this work, we adopt two metrics to measure the experimental results, i.e., the $F_{1}$ -score and intersection over union (IoU). The experimental results with comparison to traditional and deep learning approaches demonstrate the superiority of WEIN for ocean front detection.

Ocean fronts can cause the accumulation of nutrients and affect the propagation of underwater sound, so high-precision ocean front detection is of great significance to the marine fishery and national defense fields. However, the existing ocean front detection methods either have low detection accuracy or regard it as a binary classification problem to only detect where the ocean front occurs and cannot identify and distinguish various categories of ocean fronts with different behavior characteristics in different times and regions. In order to solve the above problems, we propose a semantic segmentation network called location and seasonality enhanced network (LSENet) for multiclass ocean fronts detection at pixel level. In this network, we first design a channel supervision unit (CSU) structure, which integrates the seasonal characteristics of the ocean front itself and the contextual information to improve the detection accuracy. We also introduce a location attention (LA) mechanism to adaptively assign attention weights to the fronts according to their frequently occurred sea area, which can further improve the accuracy of multiclass ocean front detection. Compared with other semantic segmentation methods and current representative ocean front detection method at Offshore China and Gulf of Mexico, the experimental results demonstrate convincingly that our method is more effective. The source code and datasets are released and publicly available via https://github.com/lliusha1155/LSENET .

In this paper, a 2-stage cascaded deep learning framework, Port Wave Prediction Network (PWPNet), is proposed for real-time prediction of significant wave height (SWH) distribution in a port. The PWP-out model of the first stage, predicting port-entrance wave parameters, utilizes three branches, the first branch using a Long Short Term Memory (LSTM) module to learn the temporal dependencies of time sequences of port-entrance wave parameters, the second branch using Wave and Wind field Feature Extraction (WWFE) modules, composed of a residual network with spatial and channel attention, to capture spatiotemporal characteristics of outside-port 2D wave and wind field data, the third branch using multi-scale time encoding to capture the periodic characteristics of waves and wind. The PWP-in model of the second stage, estimating the in-port SWH distribution, uses port-entrance wave parameters based on a customized Artificial Neural Network (ANN) and takes PWP-out’s output as its input. A comparison of the performance of PWP-out and mainstream machine learning models including LSTM, GRU, BPNN, SVR, ELM, and RF at Hambantota Port shows that PWP-out outperforms all other models regarding medium-term (25–48 h), med–long-term (49–72 h), and long-term (73–96 h) predictions, and ablation experiments proved the effectiveness of the three branches. Furthermore, the performance comparison of our PWPNet and other 2-stage models of LSTM, GRU, BPNN, SVR, ELM, and RF cascaded with PWP-in shows that PWPNet outperforms those cascaded models for medium-term to long-term predictions of SWH distribution in a port.

Ocean fronts are a mesoscale phenomenon in the ocean. It is important for fisheries, environmental protection, and military activities. Therefore, more and more attention has been attracted to ocean front detection. However, the distribution of front and non-front pixels is highly unbalanced in remote sensing images, and it is not easy to establish an effective ocean front detection algorithm with high accuracy. To alleviate these problems, we model the problem of detecting ocean fronts as an edge detection task and design a new end-to-end bi-directional progressive fusion attention network (BPFANet). Specifically, BPFANet consists of an effective backbone and a bi-directional path. The whole backbone has four stage detection blocks (SD blocks), which capture the ocean front features at different scales. Each SD block contains a side branch structure, which includes a deep residual dilated convolution (DRDC) module to enrich multi-scale edge information and an attention module (AM) to enhance the feature representation in both the channel and spatial dimensions. In addition, the bi-directional path can progressively fuse the four SD blocks of ocean front information. To evaluate BPFANet, we perform experiments on the OFDS365 dataset and show its advantages over existing ocean front detection methods.