Cloud-induced gaps in Sea Surface Temperature (SST) pose significant challenges to climate modeling and oceanographic research by causing the loss of critical spatiotemporal data. Recently, traditional deep neural network-based methods have shown promising results by effectively utilizing rich historical data. However, when confronted with extensive gaps in SST data, methods such as CNN, LSTM, and Transformer struggle to model long-range dependencies effectively or often overlook non-salient features in the gap inpainting process. To address this, we introduce a novel approach for SST completion under large-scale missing data conditions, focusing on modeling long-range dependencies by leveraging Mamba to adaptively select essential features. We propose an innovative Mamba-based inpainting network, DINMamba, for reconstructing large missing areas in SST. Specifically, to ensure physical coherence across scales, DINMamba designs a stackable recursive hierarchical Mamba block that progressively captures both stable patterns and anomalies. Finally, the fusion of these two types of information guarantees more coherent and accurate reconstructions across extensive missing regions. Experimental results on real-world datasets demonstrate the superiority of DINMamba over several state-of-the-art (SOTA) methods.Under a$68\%$missing rate, our model achieves significant improvements, reducing$RMSE$by$34.07\%$,$34.91\%$, and$37.23\%$, enhancing$R^{2}$by$5.78\%$,$7.06\%$, and$8.17\%$, increasing$SSIM$by$26.73\%$,$28.82\%$, and$31.17\%$, and boosting$PSNR$by$4.93\%$,$5.01\%$, and$5.97\%$, across noise-to-signal (N/S) ratios of 0.1, 0.2, and 0.3, respectively. Furthermore, ablation studies validate the contribution of each component and the model’s generalization capability across different seasons.

Sea surface temperature (SST) is a crucial metric in marine science, playing a pivotal role in forecasting and analyzing changes in the marine environment. However, remote sensing technologies often encounter issues where SST images are obscured by clouds, leading to data loss, thereby impacting marine environment prediction efficacy. Although many deep learning methods currently exist for reconstructing SST images, most focus on handling this task within the image domain, making it challenging to adapt to the chaotic nature of ocean systems. Additionally, most methods only model at a single scale, which limits their ability to effectively capture the complex multi-scale features in SST data. Therefore, this study proposes MSF_FNO, an image completion method based on Multi-Scale Fourier Fusion Neural Operator. MSF_FNO integrates multi-scale feature fusion and frequency domain neural operator technology to effectively overcome the limitations of single-scale feature processing and image domain reconstruction in existing methods. This approach not only captures SST frequency domain information and extracts structured features of SST images but also extracts critical features across multiple scales, ensuring global consistency and detailed features in reconstruction results. Experiments on the National Satellite Ocean Application Service (NSOAS) datasets demonstrate that MSF_FNO outperforms state-of-the-art (SOTA) methods in terms of reconstruction quality and robustness.

The Scientific Visualization Images (SVI) of Sea Surface Temperature (SST) play a pivotal role as visual resources for investigating oceanographic processes. However, they are often plagued by extensive data gaps due to objective factors like cloud cover. Additionally, their content deviates significantly from ordinary images, posing challenges for conventional completion techniques. Given the intricate nature of marine systems, completing the visualization of sea surface temperature presents several challenges. Firstly, predicting missing segments relies not only on prominent patterns but also on subtle anomalies, which are often overlooked by methods focused on extracting prominent features. Secondly, these images exhibit chaos and lack clear semantics, making it difficult for methods primarily focused on semantic extraction to effectively complete them. To address these challenges, this study presents a novel method named the Inpainting Fourier Neural Network for SVI (SVIFNN). This approach employs a twin-stream architecture to highlight both significant stability and non-significant anomalies. Notably, it incorporates a "reverse attention mechanism" in the non-significant anomalies extraction stream to preserve unconventional information. Furthermore, by cascading Fourier neural operator (FNO), it leverages frequency domain characteristics to mitigate spatial chaos. Through a frequency domain feature extraction module, it achieves an adaptive fusion of significant stability and nonsignificant anomalies. Experiment results demonstrate SVIFNN’s superiority over State-Of-The-Art (SOTA) methods, particularly under a 68% missing rate condition. Significant improvements are observed inR2(18.1%, 19.3%, and 21.8%) and reductions inRMSE(22.6%, 28.8%, and 23.8%) across different Noise-to-Signal (N/S) ratios of 0.1, 0.2, and 0.3, respectively, underscoring SVIFNN’s robustness in handling SVIs extensive data gaps. Adequate ablation experiments further validate the effectiveness of the proposed non-significant anomalies extraction stream and frequency domain operators, with the latter demonstrating superior performance for scientific visualization images compared to traditional spatial domain CNN and ViT operators.

Ocean data has sheer volume, distributed sources, and spatiotemporal correlations, resulting in difficulties for distributed information collaboration. To tackle this problem, we propose a decentralized learning framework consisting of a spatial correlation modeling scheme based on k-shot data sampling and an asynchronous communication scheme for decentralized network establishing and information exchange. The proposed framework can handle the sparse communication scenario and improve the performance of the local learning models.

The sampling in the Earth system poses challenges to meeting the assumption of independent and identically distributed samples. However, existing deep neural network forecasting models inadequately address this issue, impacting the model's generalization and stability. To tackle this problem, this paper introduces the Non-Independent Sample Bias Elimination Module and the Non-Identically Distributed Prototype Modeling Module within the classical deep neural network forecasting framework. The effectiveness of the proposed approach is validated through forecasting experiments on sea surface temperature and sea surface height in the Chinese sea region.

Sea surface temperature (SST), a crucial indicator widely applied in marine-related fields, is often observed by extensive cloud cover in satellite observations, resulting in significant data gaps. While deep learning approaches offer solutions for reconstructing SST images using ample historical data, the majority of current methods overlook the context constraints inherent in oceanographic data, such as sequential correlations and global consistency. This oversight leads to non-robust completion results in the presence of extensive data gaps. This paper proposes a robust SST reconstruction network guided by sequential correlations and global consistency, designed to handle the task of extensive missing data recovery in SST under large-scale cloud cover, namely CCG_RRN. Extensive comparison experiments and visualization results affirm the effectiveness and robustness of CCG_RRN compared with the State-Of-The-Art (SOTA) methods on the public NSOAS SST datasets.

In recent years, there has been a growing demand for the colorization of remote sensing images due to their inherent limitations caused by remote sensors, such as hazy or noisy atmospheric conditions. These factors result in the captured images needing to be clarified. Compared to ordinary images, remote sensing images present unique challenges in color recovery due to their imbalanced spatial distribution of objects. In this article, we propose a novel bidirectional layout-semantic-pixel joint decoupling and embedding network (BDEnet) following the idea of human painting to generate highly saturated color images with strong spatial consistency and object salience. The proposed BDEnet model emulates the process of human painting through a step-by-step approach. It begins by determining the overall tone of a large macroscopic region and progressively refining the local color based on this initial assessment. Specifically, BDEnet incorporates finer-grained semantics and pixel color information into a colored layout that represents a wide range of continuous areas, thereby accomplishing the colorization task. The BDEnet model operates at three scales, namely the layout (macro), semantic (medium), and pixel (micro) scales. It comprises three key modules: the multiscale feature decoupling (MFD) module, the layout-semantic-pixel multigranularity learning (MGL) module, and the semantic-pixel embedding (SPE) module. MFD module effectively reduces redundant noise from the semantic and layout scales by employing scale decoupling. This process ensures the extraction of efficient features essential for MGL. In the MGL module, three branches with different scales are employed to achieve layout division, semantic segmentation, and pixel coloring. To address the issue of insufficient category label guidance in layouts, we propose a novel approach called similar semantic merging (SSM) using a weakly supervised scheme to accomplish layout division. Finally, the SPE module incorporates stable semantic and pixel information into the layout features. This integration results in the generation of color images that exhibit strong spatial consistency, emphasize object salience, and possess high color saturation.

Sea surface temperature (SST) serves as a critical indicator of oceanic environmental changes. However, the uninterrupted observation of vast oceanic areas via remote sensing is frequently impeded by cloud cover, resulting in persistent data gaps. Consequently, the completion of SST data emerges as an essential technique. Recently, the utilization of deep neural networks (DNNs), particularly generative models, has shown promising results by effectively leveraging historical data for training. However, these approaches often concentrate on spatial domain features, neglecting the inherent chaotic nature of the ocean as a complex system, thus failing to accurately reflect the complexity of oceanic processes. Therefore, this study introduces a novel approach to SST completion focusing on frequency domain features, utilizing Fourier neural operators. We propose an innovative Data INpainting Fourier Neural Network (DINFNN), for data completion to facilitate feature learning in complex oceanic systems. Our method employs a triple-stream neural network to capture periodic steady-state features, adjacent temporal features, and current context. By integrating high-pass and low-pass filters and dynamically combining them, we adaptively extract essential frequency domain features for completion. Particularly, a two-cascaded fusion module is utilized dynamically to amalgamate these distinct attributes to form a composite feature set, encompassing both steady and dynamic frequency domain elements, aimed at achieving a comprehensive SST field reconstruction. In our experimental evaluations, our method demonstrates significant improvements under various conditions. Specifically, when the cover ratio is 68% and the Noise-to-Signal (N/S) is set to 0.1, 0.2, and 0.3, we observe enhancements of 11.2%, 10.8%, and 16.2% in R-squared ($R^{2}$), respectively. Furthermore, corresponding reductions of 7.2%, 13.1%, and 7.3% in root mean square error (RMSE) are achieved, respectively. Moreover, comprehensive ablation experiments confirm the effectiveness of each component within our method and emphasize the superiority of DINFNN over conventional operators.

Sea Surface Temperature (SST) is a significant environmental factor indicating marine revolutions, which is popularly applied in the meteorological forecasting and fishing industry. Due to the limited sensing ability and occlusion caused by clouds or ice, it is difficult to obtain complete SST data. Compared to traditional interpolation-based methods which refill missed data only referred to current SST data, inpainting-based methods have been carried out with the advantage of using historical SST images to train Generative adversarial Networks (GAN) by terms of considering SST data reconstruction task as an image inpainting task. However, different from common inpainting tasks constrained by semantics, the SST image is a scientific data visualization image without semantics but physical constraints. To address this problem, this paper proposes a multi-scale inpainting GAN-based neural networks to guarantee the physical constraint and realize reasonable SST image reconstruction. The proposed framework mainly contains two modules including the Average Estimation Module (AEM) to realize a global constraint so as not to generate excessive deviation, and the Multi-scale Anomaly Decouple Module (MSADM) to preserve data specificity of current SST image from well-designed multi-scale and decoupled perspectives. Finally, a post-fusion module concatenates the "average" and "specificity" features together to accomplish our multi-scale physical constraints SST image inpainting task. Sufficient experiments have been carried out to verify the effectiveness and physical consistency compared with prior SOTA methods applied to the public AVHRR Pathfinder SST dataset.

Cross-domain recommendation is an effective way to cope with the cold-start problem in recommendation systems. Knowledge of the current, particularly reviews, is taken into account to improve user/item embedding to reduce the neg-ative transfer that occurs during mapping processes across the source and target domains. Traditional approaches, on the other hand, typically apply review information from the source and target domain independently without consideration of user preference divergence. In this paper, we propose a novel Review-based Preference Invariance Transfer Network (RPITN) to minimize negative transfer by combining reviews from two domains. We first build a review preference invari-ance (RPI) embedding procedure to express user/item review correlations between two domains. Then, to improve the gen-eralization ability of user/item embedding and prevent negative transfer across domains, we carefully insert RPI into the embedding learning and mapping process. Extensive exper-iments on real-world datasets demonstrate the superiority of RPITN compared with other recommendation methods.

As we all know, remote sensing (RS) images contain multi-scale and numerous RS objects, along with massive and complex spatial topological relationships, such as the adjacency, proximity relations of same-scale objects, and inclusion relations of cross-scale objects. However, the existing semantic segmentation methods have never explored the cross-scale relations, which are especially important when comes to the situation that the RS objects cannot be accurately identified, they could be supplemented by the surrounding contents. To address the above concern, we propose a scale-relation joint decoupling network (SRJDN) for the semantic segmentation of RS images by simultaneously considering decoupling scales and decoupling relations to excavate more complete relationships of multi-scale RS objects. The SRJDN is performed by following three steps, namely scale decoupling (SD), relation decoupling (RD), and fine-granularity guided fusion (FGF). The SD module uses dilated convolution with different rates to decouple RS objects into different scale feature groups, from small to large scales. Afterward, the RD considers all the spatial topological relationships and decouples these relationships according to the scale, which is divided into two parts, including same-scale relation extraction (SSRE) and cross-scale relation extraction (CSRE). The SSRE establishes the graph structures at each scale independently to mine the relationships of same-scale RS objects and the CSRE constructs the graph in a unified pattern between cross-scales to explore cross-scale target relationships. Third, the FGF module regards small-scale features as fine-granularity representation and applies its attention map to guide the learning of other scale features, which could mine more reliable and comprehensive saliency information and improve the feature consistency. Numerical experiments conducted on two large-scale fine-resolution RS image datasets empirically demonstrate the robustness of the proposed joint decoupling strategy and the effectiveness of the fine-granularity guided fusion in RS image semantic segmentation tasks.