Deep-learning-based ship detection methods in Synthetic Aperture Radar (SAR) imagery are a current research hotspot. However, these methods rely on high-quality images as input, and in practical applications, SAR images are interfered with by speckle noise, leading to a decrease in image quality and thus affecting detection accuracy. To address this problem, we propose a unified framework for ship detection that incorporates a despeckling module into the object detection network. This integration is designed to enhance the detection performance, even with low-quality SAR images that are affected by speckle noise. Secondly, we propose a Multi-Scale Window Swin Transformer module. This module is adept at improving image quality by effectively capturing both global and local features of the SAR images. Additionally, recognizing the challenges associated with the scarcity of labeled data in practical scenarios, we employ an unlabeled distillation learning method to train our despeckling module. This technique avoids the need for extensive manual labeling and making efficient use of unlabeled data. We have tested the robustness of our method using public SAR datasets, including SSDD and HRSID, as well as a newly constructed dataset, the RSSDD. The results demonstrate that our method not only achieves a state-of-the-art performance but also excels in conditions with low signal-to-noise ratios.

Offshore mariculture is critical for global food security and economic development. Advances in deep learning and data-driven approaches, enable the rapid and effective monitoring of offshore mariculture distribution and changes. However, detector performance depends heavily on training data quality. The lack of standardized classifications and public datasets for offshore mariculture facilities currently hampers effective monitoring. Here, we propose to categorize offshore mariculture facilities into six types: TCC, DWCC, FRC, LC, RC, and BC. Based on these categories, we introduce a benchmark dataset called OMAD-6. This dataset includes over 130,000 instances and more than 16,000 high-resolution remote sensing images. The images with a spatial resolution of 0.6 m were sourced from key regions in China, Chile, Norway, and Egypt, from the Google Earth platform. All instances in OMAD-6 were meticulously annotated manually with horizontal bounding boxes and polygons. Compared to existing remote sensing datasets, OMAD-6 has three notable characteristics: (1) it is comparable to large, published datasets in instances per category, image quantity, and sample coverage; (2) it exhibits high inter-class similarity; (3) it shows significant intra-class diversity in facility sizes and arrangements. Based on the OMAD-6 dataset, we evaluated eight state-of-the-art methods to establish baselines for future research. The experimental results demonstrate that the OMAD-6 dataset effectively represents various real-world scenarios, which have posed considerable challenges for current instance segmentation algorithms. Our evaluation confirms that the OMAD-6 dataset has the potential to improve offshore mariculture identification. Notably, the QueryInst and PointRend algorithms have distinguished themselves as top performers on the OMAD-6 dataset, robustly identifying offshore mariculture facilities even with complex environmental backgrounds. Its ongoing development and application will play a pivotal role in future offshore mariculture identification and management.

Offshore mariculture is a critical component of China’s aquaculture sector, but its rapid expansion presents significant challenges to sustainable marine resource management. This study utilizes high-resolution remote sensing data (2017–2023) and advanced ConvNeXt V2 algorithms to quantitatively analyze the spatiotemporal dynamics of offshore mariculture and explore its spatial distribution in relation to marine functional zoning policies. Through a detailed classification of six mariculture types, this study reveals significant spatial shifts, with China’s offshore mariculture transitioning from a model characterized by a “coastal, concentrated layout” to a new paradigm of “deep-sea and far-sea expansion, multi-point distribution”. Notably, the area of deep-sea and far-sea mariculture increased by 41.8% in regions with water depths of 50 m or more from 2018 to 2022. However, in 2022, the actual mariculture area accounted for only 0.608% of the designated functional zones, while 61.79% of mariculture activities occurred outside these planned zones, indicating a considerable spatial mismatch between mariculture practices and zoning plans. This study underscores the urgent need to optimize spatial planning and regulatory frameworks to balance economic growth with environmental sustainability, offering novel insights and actionable recommendations for the coordinated development of China’s marine economy.

Accurate mapping of transparent and white agricultural plastic mulch from very-high-resolution imagery is difficult because bright roofs, roads, and bare soil resemble mulch, while narrow strips and damaged film fragment boundaries. We propose a semantic–texture complementation module (STCM) that adds a partially adapted MobileNetV2 texture encoder and attention-plus-concatenation fusion to PSPNet, DeepLabV3+, and U-Net, and ask whether one jointly selected configuration transfers across the three backbones. Using 1582 georeferenced GF-7 patches partitioned into 1 km spatial blocks with a 100 m exclusion buffer, STCM raised mean foreground F1 by 1.99, 2.30, and 0.99 percentage points for PSPNet, DeepLabV3+, and U-Net, respectively, and improved two-pixel boundary F1 for all three carriers. A capacity-matched scratch-MobileNetV2 control produced lower validation F1 than the ImageNet-initialized condition, associating the gain with pretrained texture representation rather than branch size alone. With STCM–PSPNet as the upstream model, connected components of its prediction generated box and centroid prompts for frozen SAM ViT-H, and a learned FusionNet-S head combined the domain probability with the SAM mask. In pooled summaries, FusionNet-S kept region F1 close to the no-SAM refinement head, improved two-pixel boundary F1 by 2.97 percentage points, and reduced HD95 by 1.19 pixels. A matched randomized-location prompt control retained similar region F1 but did not reproduce the boundary-F1 improvement. Together, the results show that one locked STCM configuration can transfer across the tested CNN carriers and that prediction-guided SAM fusion can strengthen boundary recovery within the evaluated single-date GF-7 domain.