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Can satellite observations detect global ocean heat content change with high resolution by deep learning?

ISPRS Journal of Photogrammetry and Remote Sensing 2025年 225卷 52-68页

作者： Su, Hua Teng, Jianchen Zhang, Feiyan Wang, An Huang, Zhanchao Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education The Academy of Digital China Fuzhou University Fuzhou350108 China National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou350108 China Department of Geography Ghent University Ghent9000 Belgium

The development of in situ observations has significantly improved ocean heat content (OHC) estimation. However, high-resolution OHC data remain limited, hindering detailed studies on mesoscale oceanic warming variability. This study used a deep learning method-Densely Deep Neural Network (DDNN) to reconstruct a high-resolution (0.25° × 0.25°) global OHC dataset for the upper 2000m ocean from 1993 to 2023, named the Ocean Projection and Extension Neural Network 0.25° (OPEN0.25°) product. This deep ocean remote sensing approach integrates multi-source remote sensing data, including Sea Surface Temperature (SST), Absolute Dynamic Topography (ADT), and Sea Surface Wind (SSW), alongside spatiotemporal coordinates and in situ observations. The DDNN model was trained using Argo-based gridded data and EN4-profile data, initially undergoing pre-training to assimilate large-scale oceanic features, followed by fine-tuning to enhance its accuracy in capturing mesoscale thermal structures. Our results demonstrate that the DDNN model achieves high accuracy across various depths. Particularly, OPEN0.25° can effectively capture detailed thermal variations in regions with complex dynamics, as well as the heat transfer processes within the ocean interior, outperforming traditional methods in resolution. The research highlights that, influenced by strong El Niño-Southern Oscillation (ENSO) events, OHC in the upper 700m of the Pacific Ocean potentially far exceeding expectations over the past decade. Through this study, OPEN0.25° has demonstrated its critical role in detecting and monitoring long-term changes in global OHC at high resolution. © 2025 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)

关键词： Submarine geophysics

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Detection and interpretation of the time-varying seasonal signals in China with multi-geodetic measurements

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Geodesy and Geodynamics 2025年第1期16卷 42-54页

作者： Liansheng Deng Yugang Xiao Qusen Chen Wei Peng Zhao Li Hua Chen Zhiwen Wu School of Electrical and Electronic Information Engineering Hubei Polytechnic UniversityHuangshi 435003China Hubei Luojia Laboratory GNSS Research CenterWuhan UniversityWuhan 430079China Changjiang Spatial Information Technology Engineering Co. Ltd.Wuhan 430010China Key Laboratory of Spatial Data Mining&Information Sharing of the Ministry of Education Fuzhou UniversityFuzhou 350108China School of Geodesy and Geomatics Wuhan UniversityWuhan 430079China The First Surveying and Mapping Institute of Hunan Province Changsha 410114China

The time-varying periodic variations in Global Navigation Satellite System(GNSS)stations affect the reliable time series analysis and appropriate geophysical *** this study,we apply the singular spectrum analysis(SSA)method to characterize and interpret the periodic patterns of GNSS deformations in China using multiple geodetic *** include 23-year observations from the Crustal Movement Observation Network of China(CMONOC),displacements inferred from the Gravity Recovery and Climate Experiment(GRACE),and loadings derived from Geophysical models(GM).The results reveal that all CMONOC time series exhibit seasonal signals characterized by amplitude and phase modulations,and the SSA method outperforms the traditional least squares fitting(LSF)method in extracting and interpreting the time-varying seasonal signals from the original time *** decrease in the root mean square(RMS)correlates well with the annual cycle variance estimated by the SSA method,and the average reduction in noise amplitudes is nearly twice as much for SSA filtered results compared with those from the LSF *** SSA analysis,the time-varying seasonal signals for all the selected stations can be identified in the reconstructed components corresponding to the first ten ***,both RMS reduction and correlation analysis imply the advantages of GRACE solutions in explaining the GNSS periodic variations,and the geophysical effects can account for 71%of the GNSS annual amplitudes,and the average RMS reduction is 15%.The SSA method has proved to be useful for investigating the GNSS timevarying seasonal *** could be applicable as an auxiliary tool in the improvement of nonlinear variations investigations.

关键词： GNSS coordinate time series Singularspectrumanalysis Time-varying seasonal signals Loading effects GRACE

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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Spatio-Temporal neighbors adaptive learning with two-point differences for ocean subsurface temperature reconstruction from 1960 to 2022

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International Journal of Digital Earth 2025年第1期18卷

作者： Wang, An Su, Hua Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education College of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education The Academy of Digital China Fuzhou University Fuzhou China National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou China Department of Geography Ghent University Ghent Belgium

Long time series and accurate subsurface temperature data in the global ocean are essential for ocean warming and climate change studies. The sparse in situ observations in the pre-Argo era hinder the reconstruction of long-time series observational data for the global ocean. This study proposes a novel Adaptive Spatio-TEmporal Neighbors with two-point differences (ASTEN) method for subsurface temperature reconstruction, which adaptively learns and adjusts spatio-temporal neighbors depending on the distribution of in situ observations to ensure robust gaps-filling performance across four dimensions. By integrating geoscience domain knowledge and utilizing spatiotemporal autocorrelation, ASTEN simultaneously learns the spatial pattern and temporal variation of subsurface temperature, and significantly enhances the interpretability and accuracy of ocean temperature reconstructions over a long time series compared to the DINCAE and DINEOF. The ASTEN reconstructed temperature data for the upper 1000 m from 1960 to 2022 can effectively track the ocean warming process for more than six decades. This study demonstrates the ASTEN method is well suited for subsurface temperature reconstruction, and holds great potential in the gaps-filling of sparse ocean observations with high missing rates over a large scale. The new reconstruction of subsurface temperature can effectively reduce the uncertainty of subsurface ocean warming analysis. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Global warming

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Land degradation sensitivity assessment and convergence analysis in Korla of Xinjiang, China

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Journal of Arid Land 2020年第4期12卷 594-608页

作者： DING Jinchen CHEN Yunzhi WANG Xiaoqin CAO Meiqin Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National&Local Joint Engineering Research Center of Satellite Geospatial Information TechnologyFuzhou UniversityFuzhou 350108China

Land degradation has a major impact on environmental and socio-economic sustainability. Scientific methods are necessary to monitor the risk of land degradation. In this study, the environmental sensitive area index(ESAI) was utilized to assess land degradation sensitivity and convergence analysis in Korla, a typical oasis city in Xinjiang of China, which is located on the northeast border of the Tarim Basin. A total of 18 indicators depicting soil, climate, vegetation, and management qualities were used to illustrate spatial-temporal patterns of land degradation sensitivity from 1994 to 2018. We investigated the causes of spatial convergence and divergence based on the Ordinary Least Squares(OLS) and Geographically Weighted Regression(GWR) models. The results show that the branch of the Tianshan Mountains and oasis plain had a low sensitivity to land degradation, while the Tarim Basin had a high risk of land degradation. More than two-thirds of the study area can be categorized as "critical" sensitivity classes. The largest percentage(32.6%) of fragile classes was observed for 2006. There was no significant change in insensitive or low-sensitivity areas, which accounted for less than 0.4% of the entire observation period. The ESAI of the four time periods(1994–1998, 1998–2006, 2006–2010, and 2010–2018) formed a series of convergence patterns. The convergence patterns of 1994–1998 and 1998–2006 can be explained by the government's efforts to "Returning Farmland to Forests" and other governance projects. In 2006–2010, the construction of afforested work intensified, but industrial development and human activities affected the convergence pattern. The pattern of convergence in most regions between 2010 and 2018 can be attributed to the government's implementation of a series of key ecological protection projects, which led to a decrease in sensitivity to land degradation. The results of this study altogether suggest that the ESAI convergence analysis is an effective e

关键词： land degradation quality index convergence analysis remote sensing environmental sensitive area index Korla

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DiverseReID: Towards generalizable person re-identification via Dynamic Style Hallucination and decoupled domain experts

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Neural Networks 2025年 189卷

作者： Jieru Jia Huidi Xie Qin Huang Yantao Song Peng Wu Institute of Big Data Science and Industry Shanxi University Taiyuan 030006 China School of Computer and Information Technology Shanxi University Taiyuan China Engineering Research Center for Machine Vision and Data Mining of Shanxi Province Shanxi University Taiyuan 030006 China

Person re-identification (re-ID) models often fail to generalize well when deployed to other camera networks with domain shift. A classical domain generalization (DG) solution is to enhance the diversity of source data so that a model can learn more domain-invariant, and hence generalizable representations. Existing methods typically mix images from different domains in a mini-batch to generate novel styles, but the mixing coefficient sampled from predefined Beta distribution requires careful manual tuning and may render sub-optimal performance. To this end, we propose a plug-and-play Dynamic Style Hallucination (DSH) module that adaptively adjusts the mixing weights based on the style distribution discrepancy between image pairs, which is dynamically measured with the reciprocal of Wasserstein distances. This approach not only reduces the tedious manual tuning of parameters but also significantly enriches style diversity by expanding the perturbation space to the utmost. In addition, to promote inter-domain diversity, we devise a Domain Experts Decoupling (DED) loss, which constrains features from one domain to go towards the orthogonal direction against features from other domains. The proposed approach, dubbed DiverseReID, is parameter-free and computationally efficient. Without bells and whistles, it outperforms the state-of-the-art on various DG re-ID benchmarks. Experiments verify that style diversity, not just the size of the training data, is crucial for enhancing generalization.

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Sandy desertification in the Belt and Road Initiative drylands during 2000–2020: Assessment under the Land Degradation Neutrality framework

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Ecological Indicators 2025年 175卷

作者： Zheng, Zhijia Dong, Yue Zhan, Hao Yu, Jinsongdi Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou350108 China The Academy of Digital China Fuzhou University Fuzhou350108 China

Sandy desertification currently poses a severe environmental challenge, hindering the socio-economic development of Belt and Road Initiative (BRI) drylands. However, due to incomplete evaluation systems and unclear driving mechanisms, research on sandy desertification in the BRI drylands remains limited. To address this issue, this study followed the Land Degradation Neutrality (LDN) framework and constructed a new index, called sandy desertification assessment index (SDAI). Based on SDAI, this study assessed the status, evolution, and driving mechanisms of sandy desertification during 2000–2020. The findings are as follows: (1) The year 2006 was identified as the turning point for light and moderate sandy desertification land, whereafter the areas of light sandy desertification increased while the areas of moderate sandy desertification decreased. (2) The transformation patterns of sandy desertification levels were similar in two decades (i.e., 2000–2010 and 2010–2020), with slight differences in conversion areas. (3) The changing trends exhibit regional variability. Notably, reversal was predominantly observed in China, Mongolia, and India, whereas intensification was concentrated in Central Asia. (4) Various factors exerted differing influences on sandy desertification, with annual cumulative precipitation, human footprint, and gross domestic product (GDP) being particularly significant. This study will provide crucial guidance for formulating prevention and control strategies in the BRI drylands. © 2025 The Authors

关键词： Sandy desertification Belt and Road Initiative Dryland Spatiotemporal distribution Driving factors

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Virtual&augmented reality for biological microscope in experiment education

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Virtual Reality & Intelligent Hardware 2020年第4期2卷 316-329页

作者： Xiang ZHOU Liyu TANG Ding LIN Wei HAN Key Laboratory of Spatial Data Mining&Information Sharing of MOE Fuzhou UniversityFuzhou 350108China National Engineering Research Center of Geospatial Information Technology Fuzhou UniversityFuzhou 350108China

Background Mixed-reality technologies,including virtual reality(VR)and augmented reality(AR),are considered to be promising potential tools for science teaching and learning processes that could foster positive emotions,motivate autonomous learning,and improve learning *** In this study,a technology-aided biological microscope learning system based on VR/AR is *** structure of the microscope is described in a detailed three-dimensional(3D)model,each component being represented with their topological interrelationships and associations among them being *** interactive behavior of the model was specified,and a standard operating guide was *** motion control of components was simulated based on collision *** with immersive VR equipment and AR technology,we developed a virtual microscope subsystem and a mobile virtual microscope guidance *** The system consisted of a VR subsystem and an AR *** focus of the VR subsystem was to simulate operating the microscope and associated interactive behaviors that allowed users to observe and operate the components of the 3D microscope model by means of natural interactions in an immersive *** AR subsystem allowed participants to use a mobile terminal that took a picture of a microscope from a textbook and then displayed the structure and functions of the instrument,as well as the relevant operating *** flexibly allowed students to use the system before or after class without time and space *** system allowed users to switch between the VR and AR *** The system is useful for helping learners(especially K-12 students)to recognize a microscope's structure and grasp the required operational skills by simulating operations using an interactive *** the future,such technology-assisted education would be a successful learning platform in an open learning space.

关键词： Virtual reality Augmented reality Microscope Operating guide Experiment

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spatial and temporal variation of land surface temperature in Fujian province from 2001 to 2015

Spatial and temporal variation of land surface temperature i...

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2018 ISPRS TC III Mid-Term Symposium on Developments, Technologies and Applications in Remote Sensing

作者： Li, Yali Wang, Xiaoqin Ding, Zhe Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National Eng. Research Center of Satellite Spatial Information Technology Fuzhou University China

Land surface temperature (LST) is an essential parameter in the physics of land surface processes. The spatiotemporal variations of LST on the Fujian province were studied using AQUA Moderate Resolution Imaging Spectroradiometer LST data. Considering the data gaps in remotely sensed LST products caused by cloud contamination, the Savitzky-Golay (S-G) filter method was used to eliminate the influence of cloud cover and to describe the periodical signals of LST. Observed air temperature data from 27 weather stations were employed to evaluate the fitting performance of the S-G filter method. Results indicate that S-G can effectively fit the LST time series and remove the influence of cloud cover. Based on the S-G-derived result, spatial and temporal Variations of LST in Fujian province from 2001 to 2015 are analysed through slope analysis. The results show that: 1) the spatial distribution of annual mean LST generally exhibits consistency with altitude in the study area and the average of LST was much higher in the east than in the west. 2) The annual mean temperature of LST declines slightly among 15 years in Fujian. 3) Slope analysis reflects the spatial distribution characteristics of LST changing trend in Fujian. Improvement areas of LST are mainly concentrated in the urban areas of Fujian, especially in the eastern urban areas. Apparent descent areas are mainly distributed in the area of Zhangzhou and eastern mountain area. © Authors 2018. CC BY 4.0 License.

关键词： Surface measurement

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