The all-weather imaging capability of synthetic aperture radar (SAR) confers unique advantages for maritime surveillance. However, ship detection under complex sea conditions still faces challenges, such as high-frequency noise interference and the limited computational power of edge computing platforms. To address these challenges, we propose a lightweight SAR small ship detection network, LWSARDet, which mitigates feature redundancy and reduces computational complexity in existing models. Specifically, based on the YOLOv5 framework, a dual strategy for the lightweight network is adopted as follows: On the one hand, to address the limited nonlinear representation ability of the original network, a global channel attention mechanism is embedded and a feature extraction module, GCCR-GhostNet, is constructed, which can effectively enhance the network’s feature extraction capability and high-frequency noise suppression, while reducing computational cost. On the other hand, to reduce feature dilution and computational redundancy in traditional detection heads when focusing on small targets, we replace conventional convolutions with simple linear transformations and design a lightweight detection head, LSD-Head. Furthermore, we propose a Position–Morphology Matching IoU loss function, P-MIoU, which integrates center distance constraints and morphological penalty mechanisms to more precisely capture the spatial and structural differences between predicted and ground truth bounding boxes. Extensive experiments conduct on the High-Resolution SAR Image Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD) demonstrate that LWSARDet achieves superior overall performance compared to existing state-of-the-art (SOTA) methods.

The basal channel is a morphological feature that widely exists at the bottom of ice shelves, which is mainly formed by the incision of warm water at the base of ice shelves. The study of the basal channel can contribute to further understanding the impact of global warming on polar ice shelves. In this article, the depression caused by the basal channel on the surface of an ice shelf is used to confirm and extract the channel's position. The automatic acquisition of the channel is realized by machine learning. To extract the basal channel more efficiently and minimize the subjectivity caused by visual interpretation, a neural network was applied for the first time, which effectively improved the accuracy of basal channel extraction. We took the manual extraction results of basal channels as the training set input from the model. The improved U-NET network model was used to classify the 79NG ice shelf area, and the extraction accuracy was 73.58%. Then, the trained model was extended to the Peterman ice shelf and Ryder ice shelf located in Greenland. The extraction accuracies were 75.54% and 72.70%, respectively.

Calving front location plays a crucial role in studying ice–ocean interaction, mapping glacier area change, and constraining ice dynamic models. However, relying solely on visual interpretation to extract annual changes in the calving front of ice shelves is a time-consuming process. In this study, a comparative analysis was conducted on the segmentation obtained from fully convolutional networks (FCN), U-Net, and U²-Net models, revealing that U²-Net exhibited the most effective classification. Notably, U²-Net outperformed the other two models by more than 30 percent in terms of the F1 parameter. Therefore, this paper introduces an automated approach that utilizes the U²-Net model to extract the calving front of ice shelves based on a Landsat image, achieving an extraction accuracy of 58 m. To assess the model’s performance on additional ice shelves in the polar region, the calving front of the Totten and Filchner ice shelves were also extracted for the past decade. The findings demonstrated that the ice velocity of the Filchner ice shelf exceeded that of the Totten ice shelf. Between February 2014 and March 2015, the majority of the calving fronts along the Filchner Ice Shelf showed an advancing trend, with the fastest-moving front measuring 3532 ± 58 m/yr.

Basal channels beneath Antarctic ice shelves are critical structural features that link ice-ocean interactions to ice shelf stability; however, their automated extraction and long-term evolution remain insufficiently understood. This study focuses on the Pine Island Ice Shelf in West Antarctica as the research area. We developed a cross-modal model which integrates U-Net and Transformer architectures to reconstruct the spatiotemporal evolution of basal channels from 2015 to 2025 using multi-source remote sensing data. Based on these results, we further analyzed the relationships between changes in basal channels, ocean-atmosphere environmental factors, and variations in surface strain. Our findings indicate that basal channels exhibited a stage-like fluctuating expansion during the study period, with the total channel length reaching a maximum of approximately 540 km by 2025. Interannual variations in channel length were significantly positively correlated with mid-year sea surface temperatures and a composite climate index, exhibiting a lag of about six months. This correlation indicates a sensitive response of basal channel evolution to oceanic and atmospheric conditions. Further analysis reveals that the expansion of basal channels was accompanied by increased surface tensile strain. Notably, in 2025, relatively high strain rates were observed even in the absence of large-scale calving events, suggesting that channel growth may have compromised the structural integrity of the ice shelf and heightened its instability risk. These findings provide valuable insights for assessing the stability of Antarctic ice shelves.