The accurate extraction and monitoring of offshore aquaculture areas are crucial for the marine economy, environmental management, and sustainable development. Existing methods relying on unimodal remote sensing images are limited by natural conditions and sensor characteristics. To address this issue, we integrated multispectral imaging (MSI) and synthetic aperture radar imaging (SAR) to overcome the limitations of single-modal images. We propose a cross-modal multidimensional frequency perception network (CMFPNet) to enhance classification and extraction accuracy. CMFPNet includes a local–global perception block (LGPB) for combining local and global semantic information and a multidimensional adaptive frequency filtering attention block (MAFFAB) that dynamically filters frequency-domain information that is beneficial for aquaculture area recognition. We constructed six typical offshore aquaculture datasets and compared CMFPNet with other models. The quantitative results showed that CMFPNet outperformed the existing methods in terms of classifying and extracting floating raft aquaculture (FRA) and cage aquaculture (CA), achieving mean intersection over union (mIoU), mean F1 score (mF1), and mean Kappa coefficient (mKappa) values of 87.66%, 93.41%, and 92.59%, respectively. Moreover, CMFPNet has low model complexity and successfully achieves a good balance between performance and the number of required parameters. Qualitative results indicate significant reductions in missed detections, false detections, and adhesion phenomena. Overall, CMFPNet demonstrates great potential for accurately extracting large-scale offshore aquaculture areas, providing effective data support for marine planning and environmental protection. Our code is available at Data Availability Statement section.

Accurately extracting offshore floating raft aquaculture (FRA) areas from remotely sensed images is the key to rationally managing aquaculture resources. Currently, deep learning-based methods perform well in FRA area extraction tasks but are limited by the shortcomings of single-modality remote sensing data, which affect their extraction accuracies. To solve this problem, we constructed a multimodal dataset called CHN-YS3-FRA for FRA area extraction using heterogeneous Sentinel-1/-2 remote sensing image data and proposed a new multiscale synthetic aperture radar (SAR) guidance network (MSARG-Net) for performing FRA area extraction on multimodal remote sensing images. In this network, we designed a global-local Poolformer block (GLPB) to model the local and global relationships of FRA areas to more comprehensively learn the semantic features of these areas. In addition, we designed a multiscale SAR-guided attention (MSARGA) block to efficiently fuse the semantic information acquired from different modalities. The experimental results obtained on the CHN-YS3-FRA dataset show that MSARG-Net could robustly extract offshore FRA regions with F1 scores, intersection-over-union (IoU) and kappa coefficient values of 91.46%, 84.26% and 89.69%, respectively. Compared with the latest remote sensing-based semantic segmentation methods, MSARG-Net has achieved significant quantitative and qualitative improvements and has significant potential for mapping and monitoring large-scale offshore FRA areas. Our code and dataset are available at Pytorch-MSARG-Net.

Accurately extracting large-scale offshore floating raft aquaculture (FRA) areas is crucial for supporting scientific planning and precise aquaculture management. While remote sensing technology offers advantages such as wide coverage, rapid imaging, and multispectral capabilities for FRA monitoring, the current methods face challenges in terms of establishing spatial–spectral correlations and extracting multiscale features, thereby limiting their accuracy. To address these issues, we propose an innovative multiscale spatial–spectral fusion network (MSSFNet) designed specifically for extracting offshore FRA areas from multispectral remote sensing imagery. MSSFNet effectively integrates spectral and spatial information through a spatial–spectral feature extraction block (SSFEB), significantly enhancing the accuracy of FRA area identification. Additionally, a multiscale spatial attention block (MSAB) captures contextual information across different scales, improving the ability to detect FRA areas of varying sizes and shapes while minimizing edge artifacts. We created the CHN-YE7-FRA dataset using Sentinel-2 multispectral remote sensing imagery and conducted extensive evaluations. The results showed that MSSFNet achieved impressive metrics: an F1 score of 90.76%, an intersection over union (IoU) of 83.08%, and a kappa coefficient of 89.75%, surpassing those of state-of-the-art methods. The ablation results confirmed that the SSFEB and MSAB modules effectively enhanced the FRA extraction accuracy. Furthermore, the successful practical applications of MSSFNet validated its generalizability and robustness across diverse marine environments. These findings highlight the performance of MSSFNet in both experimental and real-world scenarios, providing reliable, precise FRA area monitoring. This capability provides crucial data for scientific planning and environmental protection purposes in coastal aquaculture zones.

In light of global climate change and accelerated urbanization, preserving and restoring island ecosystems has become critically important. This study focuses on Changxing Island in Dalian, China, evaluating the quality of its ecological environment. The research aims to quantify ecological changes since 2000, with an emphasis on land use transformations, coastline evolution, and the driving factors behind these changes. Using the Google Earth Engine (GEE) platform and remote sensing technology, an island remote sensing ecological index (IRSEI) was developed. The development of the IRSEI was grounded in several key ecological parameters, including the normalized difference vegetation index (NDVI), wetness index (WET), land surface temperature index (LST), multiband drought stress index (M-NDBSI), and land use intensity index (LUI). The research results show that, since 2002, land use types on Changxing Island have undergone significant changes, with a notable decrease in arable land and a significant increase in built-up areas, reflecting the ongoing urbanization process. With respect to coastline changes, the total coastline length of Changxing Island steadily increased from 2002 to 2022, with an average annual growth rate of 2.15 km. This change was driven mainly by reclamation and infrastructure construction. The IRSEI analysis further revealed a clear deterioration in the quality of the ecological environment of Changxing Island during the study period. The proportion of excellent ecological area decreased from 39.3% in 2002 to 8.89% in 2022, whereas the areas classified as poor and very poor increased to 56.23 km² and 129.84 km², both of which set new historical records. These findings suggest that, as urbanization and coastline development intensify, the ecosystem of Changxing Island is at significant risk of degradation. The optimized IRSEI effectively captured the ecological environment quality of the island, improved the long-term stability of the index, and adequately met the requirements for large-scale and long-term ecological environment quality monitoring.

Island coastline segmentation plays a crucial role in remote sensing image processing, especially when the island environment is complex and the scale is small, making segmentation challenging. The complex morphology of the islands and the small islands are the main causes of boundary blurring and topological discontinuity in the segmentation of the island coast. Therefore, this study proposes GTSegNet, an island coastline segmentation method designed to address the issues of boundary blurring and topological discontinuity in complex backgrounds. First, by introducing the Graph Contextual Modeling Module (GCB), the model captures global information and addresses the issue of neglected local features due to complex backgrounds and scale differences, thereby improving the model’s ability to discern blurry boundaries. Secondly, the Morphological Topology-Aware Refinement Module (TARM) is used for boundary sharpening and false response suppression, specifically addressing the issue of topological discontinuity, thus improving the accuracy of boundary localization and the continuity of topological structures. The two modules work synergistically, significantly improving the accuracy of the boundaries and topological continuity of the island coastline. Training and comparative experiments conducted on the newly constructed island coastline dataset demonstrate that GTSegNet achieves an outstanding performance with an mIoU of 96.96% and a Recall of 98.54%. Compared to other remote sensing semantic segmentation methods, GTSegNet consistently exhibits stable advantages in both quantitative and qualitative assessments, showcasing its great potential for large-scale marine mapping and macro-scale monitoring tasks.