The development of ocean remote sensing makes it possible to obtain valuable information from a large amount of data. Deep learning is a powerful tool that is beneficial for obtaining ocean information from remote sensing data. Oceanic internal waves play an essential role in ocean activities. To obtain information on irregular stripes from Synthetic Aperture Radar (SAR) images, a stripe segmentation algorithm for oceanic internal waves is proposed based on SegNet. The research results show that the proposed method can identify whether the SAR images contain oceanic internal waves and obtain the respective locations of the light and dark stripes of the oceanic internal waves from SAR images. Furthermore, because this method can accurately determine the relative locations of the light and dark stripes, it can distinguish the moment when oceanic internal waves undergo polarity conversion.

Oceanic internal waves play a crucial role in ocean activities. Currently, the approach to detecting oceanic internal waves from synthetic aperture radar (SAR) images is becoming robust. To efficiently identify the stripes of oceanic internal waves from SAR images, we propose an integrated algorithm for the detection and recognition of oceanic internal waves. First, the Gamma Map filtering method was adopted to reduce speckle noise in the SAR images. Then, histogram of orientated gradients (HOG), Grey level co-occurrence matrix (GLCM), and fractal dimension (FD) were utilized to extract the image features. Subsequently, support vector machine (SVM) was adopted to classify the SAR images and obtain images that contain oceanic internal waves. Next, the Canny edge detection method was used to detect and recognize the stripes of oceanic internal waves in the SAR images, and these stripes were screened by three parameters, namely their lengths, area ratios, and directions. Finally, the positions of the stripes of the oceanic internal waves were obtained. The experimental results verify that the proposed method can identify whether SAR images contain oceanic internal waves, and also determine the locations of their stripes in the SAR images. Meanwhile, the algorithm exhibits reasonable robustness and recognition rate. In addition, the optimal accuracy and kappa coefficient ( $\kappa$) are 94.2% and 0.878, respectively.

This study presents an in-depth analysis of the dynamic beach landscapes of Hainan Island, which is located at the southernmost tip of China. Home to over a hundred natural and predominantly sandy beaches, Hainan Island confronts significant challenges posed by frequent marine natural disasters and human activities. Addressing the urgent need for long-term studies of beach dynamics, this research involved the use of CoastSat to extract and analyze shoreline data from 20 representative beaches and calculate the slopes of 119 sandy beaches around the island for the period from 2013 to 2023. The objective was to delineate the patterns of beach evolution that contribute to the prevention of sediment loss, the mitigation of coastal hazards, and the promotion of sustainable coastal zone management. By employing multi-source remote sensing imagery and the CoastSat tool, this investigation validated slope measurements across selected beaches, demonstrating consistency between the calculated and actual distances despite minor anomalies. The effective use of the finite element solution (FES) in the 2014 global tidal model for tidal corrections further aligned the coastlines with the mean shoreline, underscoring CoastSat’s utility in enabling precise coastal studies. The analysis revealed significant seasonal variations in shoreline positions, with approximately half of the monitored sites showing a seaward progression in summer and a retreat in winter, which were linked to variations in wave height. The southern beaches exhibited distinct seasonal variations, which contrasted with the general trend due to differing wave impacts. The western and southern shores showed erosion, while the northern and eastern shores displayed accretion. The calculated slopes across the island indicated that the southern beaches had steeper slopes, while the northern areas exhibited more pronounced slope variations due to wave and tidal impacts. These findings highlight the critical role of integrated coastal management and erosion control strategies in safeguarding Hainan Island’s beaches. By understanding the mechanisms driving seasonal and regional shoreline changes, effective measures can be developed to mitigate the impacts of erosion and enhance the resilience of coastal ecosystems amidst changing environmental conditions. This research provides a foundational basis for future efforts aimed at the sustainable development and utilization of coastal resources on Hainan Island.

The development of deep learning and remote sensing makes it possible to obtain information about oceanic internal waves from massive data. Oceanic internal waves play an important role in ocean activities. To obtain the light and dark stripes from synthetic aperture radar (SAR) images, a stripe segmentation algorithm for oceanic internal waves is proposed based on Mask R-CNN. The research results show that the proposed method can not only identify whether there are oceanic internal waves, but also obtain the respective locations of the light and dark stripes in the images. The width and the direction angle of each light and dark stripe can be obtained efficiently by a separation and matching method with the sector region (SMMSR). Moreover, based on the identified relative locations of the light and dark stripes, it is possible to further determine the time when the oceanic internal waves undergo polarity conversion.

The study of oceanic internal waves remains a critical area of research within oceanography. With the rapid advancements in oceanic remote sensing and deep learning, it is now possible to extract valuable insights from vast datasets. In this context, by building datasets using deep learning models, we propose a novel stripe segmentation algorithm for oceanic internal waves, leveraging synthetic aperture radar (SAR) images based on the SegFormer architecture. Initially, a hierarchical transformer encoder transforms the image into multilevel feature maps. Subsequently, information from various layers is aggregated through a multilayer perceptron (MLP) decoder, effectively merging local and global contexts. Finally, a layer of MLP is utilized to facilitate the segmentation of oceanic internal waves. Comparative experimental results demonstrated that SegFormer outperformed other models, including U-Net, Fast-SCNN (Fast Segmentation Convolutional Neural Network), ORCNet (Ocular Region Context Network), and PSPNet (Pyramid Scene Parsing Network), efficiently and accurately segmenting marine internal wave stripes in SAR images. In addition, we discuss the results of oceanic internal wave detection under varying settings, further underscoring the effectiveness of the algorithm.