The objective of super-resolution in remote sensing imagery is to enhance low-resolution images to recover high-quality details. With the rapid progress of deep learning technology, the deep learning-based super-resolution technology for remote sensing images has also made remarkable achievements. However, these methods encounter several challenges. They often struggle with processing long-range spatial information that encompasses complex scene changes, adversely affecting the image's coherence and accuracy. Furthermore, the lack of connectivity in feature extraction blocks hinders effective feature utilization in deeper network layers, leading to issues such as gradient vanishing and exploding. Additionally, constraints in the spatial domain of previous methods frequently result in severe shape distortion and blurring. To address these issues, this study proposes the CFFormer, a new super-resolution framework that employs the Swin Transformer as its core architecture and incorporates the Channel Fourier Block (CFB) to refine features in the frequency domain. The Global Attention Block (GAB) is also integrated to enhance global information capture, thereby improving the extraction of spatial features. To increase model stability and feature utilization efficiency, a Jump-Joint Fusion Mechanism is designed, culminating in a Residual Fusion Swin Transformer Block (RFSTB) that alleviates the gradient vanishing issue and optimizes feature reuse. Experimental results confirm the CFFormer's superior performance in remote sensing image reconstruction, demonstrating outstanding perceptual quality and reliability. Notably, the CFFormer achieves a Peak Signal-to-Noise Ratio (PSNR) of 29.83 dB on the UcMercedx4 dataset, surpassing the SwinIR method by approximately 0.5 dB, indicating a substantial enhancement.

With the development of remote sensing technology, content-based remote sensing image retrieval has become a research hotspot. Remote sensing image datasets not only contain rich location, semantic and scale information but also have large intra-class differences. Therefore, the key to improving the performance of remote sensing image retrieval is to make full use of the limited sample information to extract more comprehensive class features. In this paper, we propose a proxy-based deep metric learning method and an adaptive multi-proxy framework. First, we propose an intra-cluster sample synthesis strategy with a random factor, which uses the limited samples in batch to synthesize more samples to enhance the network’s learning of unobvious features in the class. Second, we propose an adaptive proxy assignment method to assign multiple proxies according to the cluster of samples within a class, and to determine weights for each proxy according to the cluster scale to accurately and comprehensively measure the sample-class similarity. Finally, we incorporate a rigorous evaluation metric mAP@R and a variety of dataset partitioning methods, and conduct extensive experiments on commonly used remote sensing image datasets.

Islands with both land and sea characteristics provide the foundation for protecting the marine environment, preserving the ecological balance of the ocean, and fostering sustainable economic and social growth. Advanced monitoring technologies have boosted the collection of multi-source data of islands, but the isolation and insufficiency of data are harmful to the development and management of islands. To form a coherent and complete understanding of islands, it is necessary to convert the multi-source data into knowledge-based information. This paper proposes an island knowledge graph construction method based on the combination of entity dictionary and rule patterns, and builds the island knowledge graph (ISLKG) from the top to bottom. An ontology layer is initially created to standardize island knowledge, followed by the collection, transformation, and extraction of entities and relationships from multi-source data in order to construct an island knowledge graph. Then, a knowledge reasoning model based on knowledge graph embedding is used for knowledge completion, improving the ISLKG. Finally, the knowledge inference model was verified based on the constructed island knowledge graph. The results indicate that the model can effectively predict missing entities and complement the island knowledge graph.

Accurately predicting the trajectories of mesoscale eddies is essential for comprehending the distribution of marine resources and the multiscale energy cascade in the ocean. Nevertheless, current approaches for predicting mesoscale eddy trajectories frequently exhibit inadequate examination of the intrinsic multiscale temporal data, resulting in diminished predictive precision. To address this challenge, our research introduces an enhanced transformer-based framework for predicting mesoscale eddy trajectories. Initially, a multivariate dataset of mesoscale eddy trajectories is constructed and expanded, encompassing eddy properties and pertinent ocean environmental information. Additionally, novel feature factors are delineated based on the physical attributes of eddies. Subsequently, a multi-head attention mechanism is introduced to bolster the modeling of the multiscale time-varying connections within eddy trajectories. Furthermore, the original positional encoding is substituted with Time-Absolute Position Encoding, which considers the dimensions and durations of the sequence mapping, thereby improving the distinguishability of embedded vectors. Ultimately, the Soft-DTW loss function is integrated to more accurately assess the overall discrepancies among mesoscale eddy trajectories, thereby improving the model’s resilience to erratic and diverse trajectory sequences. The effectiveness of the proposed framework is assessed using the eddy-abundant South China Sea. Our framework exhibits exceptional predictive accuracy, achieving a minimum central error of 8.507 km over a seven-day period, surpassing existing state-of-the-art models.

Deep metric learning has been widely used in many visual tasks. Its key idea is to increase the similarity of positive samples and decrease the similarity of negative samples through network training. To achieve this purpose, many studies excessively extend the distance between the query sample and hard negative samples. This may compress the distance between similar samples of other classes, causing these samples to cluster together. We call this phenomenon Negative Sample Aggregation. To address this problem, first, we propose a weighting method based on the Ranking Similarity of sample pairs, short for RS. The proposed weighting method can not only enlarge the distance between the query sample and hard negative samples, but also maintain the embedding distribution of proximal negative samples. Second, we propose a Top-nk sampling method, which can dynamically adjust the sampling strategy according to the distribution of a dataset. It solves the problem that the descent direction of the network gradient is inconsistent with the optimization target. The effectiveness of our methods is evaluated by extensive experiments on four public datasets and compared with that of other state-of-the-art methods. The results show that the proposed method obtains excellent performance, reaching 67.8% on CUB-200-2011 and 85.2% on Cars-196 at Recall@1.