Coastline extraction from remote sensing imagery is persistently challenged by intra-class heterogeneity (e.g., diverse coastline types) and boundary ambiguity. Existing methods often exhibit suboptimal performance in complex scenes mixing artificial and natural landforms, as they tend to ignore coastline morphological priors and struggle to recover details in low-contrast regions. To address these issues, this paper introduces TopoSeg-Net, a novel collaborative framework centered on a dual-decoder architecture. A segmentation decoder utilizes a Morphology-Aware Attention (MAA) module to adaptively decouple and model diverse coastline morphologies, and a Structure-Detail Synergistic Enhancement (SDSE) module to reconstruct weak boundaries with high fidelity. Meanwhile, a learnable topology decoder frames topology construction as a graph reasoning task, which ensures the geometric and topological integrity of the final vector output.TopoSegNet was evaluated on the public Landsat-8 and a custom Lianyungang Gaofen-1 (GF-1) dataset. The experimental results show that the proposed method reached 98.64%, 66.80%, and 0.795 on the mIoU, BIoU, and APLS metrics, respectively, verifying its validity and superiority. Compared to state-of-the-art methods, the TopoSegNet model demonstrates significantly higher accuracy and topological fidelity.

Accurate monitoring of Golden Tides (Sargassum Harmful Algal Blooms) is critical for preventing and mitigating marine ecological disasters. Image-level weakly supervised semantic segmentation (WSSS) methods employ image-level labels to derive pixel-level predictions. While they considerably reduce annotation costs and accelerate deployment compared to fully supervised approaches, they still encounter several challenges: Golden Tide occurs as irregular floating patches at sea and class activation maps (CAMs) focus only on the most discriminative regions, many discrete targets are overlooked and pseudo-labels remain incomplete. Moreover, complex marine backgrounds distract model attention and introduce semantic noise, reducing pseudo-label reliability and hindering detection accuracy from meeting practical requirements. We propose MPECAM, a multi-prototype-enhanced weakly supervised framework for Golden Tide detection, which consists of three main components: (i) Multi-Prototype Fusion (MPF) strategy and Multi-Prototype Re-Activation (MPRA) module, which dynamically maintain a prototype bank and reweight feature maps to activate weak-response regions, thereby enhancing completeness for irregular targets; (ii) Inter-Prototype Contrastive Constraint (IC2) module, which employs a foreground-global-background triplet contrastive loss to decoupling Golden Tide feature from seawater features, suppress semantic confusion, and refine pseudo-label purity; (iii) a self-supervised consistency mechanism, which leverages the refined CAM as supervision to reintegrate structurally complete pseudo-label knowledge back into the backbone, enabling joint optimization of the modules. Experimental results on hybrid sensor remote sensing imagery covering the Yellow and East China Seas demonstrate that our method achieves state-of-the-art performance (59.25% mIoU and 74.41% F1-score), providing reliable segmentation of Golden Tide regions and offering a scalable solution for marine HAB agile monitoring.

Fine-grained classification of coastal wetlands from remote sensing images is a challenging task due to the spectral overlap between different wetland vegetation types, making them difficult to distinguish. Traditional methods for remote sensing interpretation often rely on manual classification or shallow machine learning approaches, which fail to effectively capture complex spatial relationships and contextual information. To integrate ecological and biological prior knowledge and enhance the generalization performance of the model, this paper proposes a remote sensing image classification method for coastal wetlands based on Category Representation Learning, called CRLNet. The core idea is to learn category-invariant representations of land cover types in coastal wetlands using geoscience knowledge graphs and deep neural networks. First, deep feature maps and classification probability maps generated by a semantic segmentation network are used to initialize the representations of each category; then, the Spatial Topological Relationship Encoder (STRE) and Category Attribute Knowledge Encoder (CAKE) are proposed, employing a two-stream architecture to refine the representations of each category; finally, each pixel is assigned to the category with the highest similarity based on the aforementioned deep feature maps and category-invariant representations. By combining graph convolution and self-attention mechanisms, CRLNet effectively integrates ecological and biological prior knowledge into category representation learning, thereby reducing the likelihood of conflicts between classification results and geoscience prior knowledge. Experimental results demonstrate that CRLNet outperforms state-of-the-art methods on the Huanghe River and Yancheng coastal wetland datasets. Notably, CRLNet is a lightweight framework with only 1/55 of the parameter count of CGGLNet, making it computationally efficient while maintaining high classification accuracy. The codes will be available from the website: https://github.com/cuibinge/CRLNet.

Accurate monitoring of raft aquaculture areas (RAAs) is particularly important for the protection of marine ecosystems. However, existing semantic segmentation methods are often degraded by severe shrinkage when extracting inapparent RAAs caused by natural factors such as tide level changes and human activities including laver harvesting. In this letter, a graph-based boundary-interior interaction network (BI2Net) is proposed for laver RAA extraction. Graph convolution based on soft clustering is introduced to capture the global distribution patterns among RAAs. For the network structure, we designed the RAA-boundary branch and RAA-interior branch, one for locating the boundaries and the other for extracting the RAAs. Importantly, unlike traditional dual-branch methods that only use additional information from the auxiliary tasks to assist with the primary task, BI2Net introduces a graph interaction module (GIM) that implements reasoning about the relationship between differ-ent distribution patterns, which sufficiently and comprehensively exploits mutual benefits between RAA extraction and boundary detection. After the addition of GIM, precision, recall, F1 and IoU were improved by 2.0%, 8.2%, 0.060 and 8.2% respectively.Extensive experiments have shown that our proposed BI2Net outperforms existing methods in terms of the consistency and completeness of RAA extraction, with precision, recall, F1 and IoU reached 93.0%, 90.6%, 0.916 and 84.7%, respectively.

The fine mapping of coastal wetlands is a major challenge due to the spectral aliasing of vegetation. In this letter, we selected Zhuhai-1 hyperspectral images (HSIs) for coastal wetland mapping and proposed a tiny-scene embedding network (TSE-Net) based on scene representation and attention mechanism. In TSE-Net, the tiny-scene representation associated with each hyperspectral pixel was extracted and used to enhance the spectral discrimination of ground objects. DenseNet was chosen as the backbone network, and the attention mechanism was introduced into the dense blocks to extract remarkable features. Experiments on the Yellow River estuary coastal wetland showed that the results of TSE-Net had a significant improvement in accuracy compared to other models, especially for the coastal wetland vegetation with confusing spectra, such as Spartina alterniflora, Suaeda salsa, Phragmites australis, and Tamarix.