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学者姓名：苏华
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Abstract ：
Building extraction from remote sensing imagery is vital for various human activities. But it is challenging due to diverse building appearances and complex backgrounds. Research shows the importance of both global context and spatial details for accurate building extraction. Therefore, methods integrating convolutional neural networks (CNNs) and visual transformers (ViTs) are popular nowadays. However, current methods combining these two methods inadequately merge their features and only perform decoding once, leading to issues like unclear boundaries, internal voids, and susceptibility to non-building elements in complex scenarios with low inter-class and high intra-class variability. To address these issues, this paper introduces a novel extraction method called TDFNet. We first replace ViT with V-Mamba, which has linear complexity, and combine it with CNN for feature extraction. A bidirectional fusion module (BFM) is then designed to comprehensively integrate spatial details and global information, thereby enabling accurate identification of boundaries between adjacent buildings, and maintaining the structural integrity of buildings to avoid internal holes. During the decoding process, we propose an Encoder-Decoder Fusion Module (EDFM) to initially merge features from different stages of the encoder and decoder, thereby diminishing the model's susceptibility to non-building elements with features similar to those of buildings, and consequently reducing the incidence of erroneous extractions. Subsequently, a twice decoding strategy is implemented to enhance the learning of multi-scale features significantly, thereby mitigating the impact of tree occlusions and shadows. Our method yields the state-of-the-art (SOTA) performance on three public building datasets.
Keyword ：
Building extraction Building extraction remote sensing remote sensing twice decoding twice decoding V-Mamba V-Mamba
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| GB/T 7714 | Wang, Wenlong , Yu, Peng , Li, Mengmeng et al. TDFNet: twice decoding V-Mamba-CNN Fusion features for building extraction [J]. | GEO-SPATIAL INFORMATION SCIENCE , 2025 . |
| MLA | Wang, Wenlong et al. "TDFNet: twice decoding V-Mamba-CNN Fusion features for building extraction" . | GEO-SPATIAL INFORMATION SCIENCE (2025) . |
| APA | Wang, Wenlong , Yu, Peng , Li, Mengmeng , Zhong, Xiaojing , He, Yuanrong , Su, Hua et al. TDFNet: twice decoding V-Mamba-CNN Fusion features for building extraction . | GEO-SPATIAL INFORMATION SCIENCE , 2025 . |
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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.
Keyword ：
data gaps-filling data gaps-filling Ocean subsurface temperature Ocean subsurface temperature spatio-temporal neighbors adaptive learning spatio-temporal neighbors adaptive learning subsurface ocean warming subsurface ocean warming time series reconstruction time series reconstruction
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| GB/T 7714 | Wang, An , Su, Hua . Spatio-Temporal neighbors adaptive learning with two-point differences for ocean subsurface temperature reconstruction from 1960 to 2022 [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
| MLA | Wang, An et al. "Spatio-Temporal neighbors adaptive learning with two-point differences for ocean subsurface temperature reconstruction from 1960 to 2022" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 18 . 1 (2025) . |
| APA | Wang, An , Su, Hua . Spatio-Temporal neighbors adaptive learning with two-point differences for ocean subsurface temperature reconstruction from 1960 to 2022 . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
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To scientifically plan and accurately manage the coastal aquaculture industry, it is especially critical to quickly and accurately extract raft aquaculture areas. In the study, the Raft-Former was designed to accurately extract coastal raft aquaculture in Sansha Bay using Sentinel-2 remote sensing imagery. Specifically, a Feature Enhancement Module (FEM) was designed to selectively learn the interest features for solving the omission and mis-extraction caused by changes in the coastal environment. For the boundary adhesion problems caused by the dense distribution of raft aquaculture areas, a Feature Alignment Module (FAM) was developed to enhance edge-aware ability. A Global-Local Fusion Module (GLFM) was introduced to effectively integrate the local features with multi-scale and global features to overcome significant scale differences in aquaculture areas. Numerous experiments show that our method is better than the state-of-the-art models. Specifically, Raft-Former respectively achieves 90.05% and 86.73% mIoU on the Sansha Bay dataset.
Keyword ：
edge-aware edge-aware multi-scale multi-scale Raft extraction Raft extraction Sentinel-2 remote sensing imagery Sentinel-2 remote sensing imagery
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| GB/T 7714 | Su, Hua , Liu, Yuxin , Huang, Zhanchao et al. Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
| MLA | Su, Hua et al. "Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 18 . 1 (2025) . |
| APA | Su, Hua , Liu, Yuxin , Huang, Zhanchao , Wang, An , Hong, Wenjun , Cai, Junchao . Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
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Sea ice change detection is vital for understanding climate dynamics and ensuring maritime safety. Existing deep learning methods often struggle with the significant impact of color variations in satellite imagery, which can lead to inaccurate detection results. Moreover, the scarcity of labeled sea ice change data limits the ability of models to generalize across diverse scenarios. To address these challenges, we propose SICNet, a sea ice change detection model with enhanced color robustness and data efficiency. A wavelet-guided color-robust fusion (WCF) module is introduced to reduce low-frequency color discrepancies while preserving high-frequency edge details. In addition, a novel change-sensitive CutMix (CSC) strategy is used to augment training samples by focusing on regions with moderate changes, effectively increasing data diversity. Experiments conducted on our constructed sea ice change dataset demonstrate that SICNet achieves superior performance and robustness under varying environmental and lighting conditions. © 2004-2012 IEEE.
Keyword ：
Change detection Change detection Climate change Climate change Color Color Deep learning Deep learning Labeled data Labeled data Learning systems Learning systems Sampling Sampling Satellite imagery Satellite imagery Sea ice Sea ice Wavelet transforms Wavelet transforms
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| GB/T 7714 | Hong, Wenjun , Huang, Zhanchao , Yang, Yongke et al. Color-Robust Sea Ice Change Detection [J]. | IEEE Geoscience and Remote Sensing Letters , 2025 , 22 . |
| MLA | Hong, Wenjun et al. "Color-Robust Sea Ice Change Detection" . | IEEE Geoscience and Remote Sensing Letters 22 (2025) . |
| APA | Hong, Wenjun , Huang, Zhanchao , Yang, Yongke , Cai, Junchao , Guan, Weiwang , Zhou, Jiajun et al. Color-Robust Sea Ice Change Detection . | IEEE Geoscience and Remote Sensing Letters , 2025 , 22 . |
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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 degrees x 0.25 degrees) global OHC dataset for the upper 2000m ocean from 1993 to 2023, named the Ocean Projection and Extension Neural Network 0.25 degrees (OPEN0.25 degrees) 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 degrees 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 Nino-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 degrees has demonstrated its critical role in detecting and monitoring long-term changes in global OHC at high resolution.
Keyword ：
densely deep neural network densely deep neural network high-resolution high-resolution ocean heat content ocean heat content OPEN0.25 degrees dataset OPEN0.25 degrees dataset satellite observations satellite observations
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| GB/T 7714 | Su, Hua , Teng, Jianchen , Zhang, Feiyan et al. Can satellite observations detect global ocean heat content change with high resolution by deep learning? [J]. | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2025 , 225 : 52-68 . |
| MLA | Su, Hua et al. "Can satellite observations detect global ocean heat content change with high resolution by deep learning?" . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 225 (2025) : 52-68 . |
| APA | Su, Hua , Teng, Jianchen , Zhang, Feiyan , Wang, An , Huang, Zhanchao . Can satellite observations detect global ocean heat content change with high resolution by deep learning? . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2025 , 225 , 52-68 . |
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Subsurface density (SD) is a crucial dynamic environment parameter reflecting a 3-D ocean process and stratification, with significant implications for the physical, chemical, and biological processes of the ocean environment. Thus, accurate SD retrieval is essential for studying dynamic processes in the ocean interior. However, complete spatiotemporally accurate SD retrieval remains a challenge in terms of the equation of state and physical methods. This study proposes a novel multiscale mixed residual transformer (MMRT) neural network method to compensate for the inadequacy of the existing methods in dealing with spatiotemporal nonlinear processes and dependence. Considering the spatial correlation and temporal dependence of dynamic processes within the ocean, the MMRT addresses temporal dependence by fully using the transformer's processing of time-series data and spatial correlation by compensating for deficiencies in spatial feature information through multiscale mixed residuals. The MMRT model was compared with the existing random forest (RF) and recurrent neural network (RNN) methods. The MMRT model achieves the best accuracy with an average determination coefficient (R-2) of 0.988 and an average root mean square error (RMSE) of 0.050 kg/m(3) for all layers. The MMRT model not only outperforms the RF and RNN methods regarding reliability and generalization ability when estimating global ocean SD from remote sensing data but also has a more interpretable encoding process. The MMRT model offers a new method for directly estimating SD using multisource satellite observations, providing significant technical support for future remote sensing super-resolution and prediction of subsurface parameters.
Keyword ：
Global ocean Global ocean remote sensing observations remote sensing observations subsurface density (SD) subsurface density (SD) transformer transformer
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| GB/T 7714 | Su, Hua , Qiu, Junlong , Tang, Zhiwei et al. Retrieving Global Ocean Subsurface Density by Combining Remote Sensing Observations and Multiscale Mixed Residual Transformer [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Su, Hua et al. "Retrieving Global Ocean Subsurface Density by Combining Remote Sensing Observations and Multiscale Mixed Residual Transformer" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Su, Hua , Qiu, Junlong , Tang, Zhiwei , Huang, Zhanchao , Yan, Xiao-Hai . Retrieving Global Ocean Subsurface Density by Combining Remote Sensing Observations and Multiscale Mixed Residual Transformer . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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As a result of global warming, major ocean basins have witnessed an increase in the number of extreme warm events and a decrease in the number of extreme cold events, increasing the number of marine heatwave (MHW) events. Previous quantification of MHW events has been limited to simple single metrics, which can only recognize some characteristics from a particular aspect. Here, we propose a new marine Heat Wave Comprehensive Index (HWCI) by fusing multiple metrics to characterize the scalable cumulative intensity of MHWs, which exhibits excellent identification reliability and superiority to effectively monitor the evolutionary patterns of various levels of MHW events. We find that five levels of global MHW events have presented an obvious spatial expansion and temporal enhancement pattern since the early 21st century, with the obvious spatial contraction (32.98 %) of weak events followed by the expansion (19.82 %) of extreme events at the highest growth rate of 0.07, primarily in the mid-low-latitude oceans and the Arctic. The results demonstrate that extreme MHW events dominate global MHW evolution patterns and that the expansion and intensification of such episodes have major implications for the event distribution and level structure. The new indicator is promising for directly measuring and identifying MHWs, and contributes to a more comprehensive understanding of the evolution of MHWs in the context of global climate change.
Keyword ：
Climate extremes Climate extremes Global ocean warming Global ocean warming Heat wave comprehensive index (HWCI) Heat wave comprehensive index (HWCI) Marine heat wave (MHW) Marine heat wave (MHW)
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| GB/T 7714 | Chen, Yingliang , Su, Hua , Yan, Xiao-Hai et al. Global oceans suffer extreme heatwaves intensifying since the early 21st century: A new comprehensive index [J]. | ECOLOGICAL INDICATORS , 2024 , 162 . |
| MLA | Chen, Yingliang et al. "Global oceans suffer extreme heatwaves intensifying since the early 21st century: A new comprehensive index" . | ECOLOGICAL INDICATORS 162 (2024) . |
| APA | Chen, Yingliang , Su, Hua , Yan, Xiao-Hai , Zhang, Hongsheng , Wang, Yunpeng . Global oceans suffer extreme heatwaves intensifying since the early 21st century: A new comprehensive index . | ECOLOGICAL INDICATORS , 2024 , 162 . |
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Tidal wetlands provide a variety of ecosystem services to coastal communities but suffer severe losses due to anthropogenic activities in the Yangtze River Estuary (YRE). However, the detailed dynamics of tidal wetlands have not been well studied with sufficient spatiotemporal resolution. Here, we proposed a rapid classification method that integrates the COntinuous monitoring of Land Disturbance (COLD) algorithm and Median Composite (MC) based on the dense Landsat time series to track the dynamic processes of tidal wetlands in the YRE from 1990 to 2020. The results showed that the COLD-MC demonstrated remarkable effectiveness in detecting the change of tidal wetlands and excellent overall accuracy and kappa coefficient ranging from 90% to 96% and 0.89-0.95, respectively. The overall accuracy of change detection was 97% with an absolute error of 0.4 years. We found that the total area of tidal wetlands experienced a net loss of 59.75 km2 in the YRE, but the gain and loss of the study period were 1556.07 and 1615.82 km2, respectively. Land reclamation, sediment reduction, and Spartina alterniflora invasion pose significant threats to tidal wetlands. Sustainable management could be implemented through the establishment of nature reserves and ecological sediment enhancement engineering projects.
Keyword ：
COLD-MC COLD-MC dynamic equilibrium dynamic equilibrium land reclamation land reclamation landsat time-series landsat time-series sediment starvation sediment starvation Tidal wetlands Tidal wetlands
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| GB/T 7714 | Wu, Wenting , Lin, Zhibin , Chen, Chunpeng et al. Tracking the dynamics of tidal wetlands with time-series satellite images in the Yangtze River Estuary, China [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2024 , 17 (1) . |
| MLA | Wu, Wenting et al. "Tracking the dynamics of tidal wetlands with time-series satellite images in the Yangtze River Estuary, China" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 17 . 1 (2024) . |
| APA | Wu, Wenting , Lin, Zhibin , Chen, Chunpeng , Chen, Zuoqi , Zhao, Zhiyuan , Su, Hua . Tracking the dynamics of tidal wetlands with time-series satellite images in the Yangtze River Estuary, China . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2024 , 17 (1) . |
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The accurate extraction of agricultural parcels from remote sensing images is crucial for advanced agricultural management and monitoring systems. Existing methods primarily emphasize regional accuracy over boundary quality, often resulting in fragmented outputs due to uniform crop types, diverse agricultural practices, and environmental variations. To address these issues, this paper proposes DSTBA-Net, an end-to-end encoder-decoder architecture. Initially, we introduce a Dual-Stream Feature Extraction (DSFE) mechanism within the encoder, which consists of Residual Blocks and Boundary Feature Guidance (BFG) to separately process image and boundary data. The extracted features are then fused in the Global Feature Fusion Module (GFFM), utilizing Transformer technology to further integrate global and detailed information. In the decoder, we employ Feature Compensation Recovery (FCR) to restore critical information lost during the encoding process. Additionally, the network is optimized using a boundary-aware weighted loss strategy. DSTBA-Net aims to achieve high precision in agricultural parcel segmentation and accurate boundary extraction. To evaluate the model's effectiveness, we conducted experiments on agricultural parcel extraction in Denmark (Europe) and Shandong (Asia). Both quantitative and qualitative analyses show that DSTBA-Net outperforms comparative methods, offering significant advantages in agricultural parcel extraction.
Keyword ：
agricultural parcel extraction agricultural parcel extraction boundary-aware weighted loss boundary-aware weighted loss dual-stream feature extraction (DSFE) dual-stream feature extraction (DSFE) feature compensation restoration (FCR) feature compensation restoration (FCR) global feature fusion module (GFFM) global feature fusion module (GFFM)
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| GB/T 7714 | Xu, Weiming , Wang, Juan , Wang, Chengjun et al. Utilizing Dual-Stream Encoding and Transformer for Boundary-Aware Agricultural Parcel Extraction in Remote Sensing Images [J]. | REMOTE SENSING , 2024 , 16 (14) . |
| MLA | Xu, Weiming et al. "Utilizing Dual-Stream Encoding and Transformer for Boundary-Aware Agricultural Parcel Extraction in Remote Sensing Images" . | REMOTE SENSING 16 . 14 (2024) . |
| APA | Xu, Weiming , Wang, Juan , Wang, Chengjun , Li, Ziwei , Zhang, Jianchang , Su, Hua et al. Utilizing Dual-Stream Encoding and Transformer for Boundary-Aware Agricultural Parcel Extraction in Remote Sensing Images . | REMOTE SENSING , 2024 , 16 (14) . |
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Estimating the ocean mixed layer depth (MLD) is crucial for studying the atmosphere-ocean interaction and global climate change. Satellite observations can accurately estimate the MLD over large scales, effectively overcoming the limitation of sparse in situ observations and reducing uncertainty caused by estimation based on in situ and reanalysis data. However, combining multisource satellite observations to accurately estimate the global MLD is still extremely challenging. This study proposed a novel Residual Convolutional Gate Recurrent Unit (ResConvGRU) neural networks, to accurately estimate global MLD along with multisource remote sensing data and Argo gridded data. With the inherent spatiotemporal nonlinearity and dependence of the ocean dynamic process, the proposed method is effective in spatiotemporal feature learning by considering temporal dependence and capturing more spatial features of the ocean observation data. The performance metrics show that the proposed ResConvGRU outperforms other well-used machine learning models, with a global determination coefficient (R2) and a global root mean squared error (RMSE) of 0.886 and 17.83 m, respectively. Overall, the new deep learning approach proposed is more robust and advantageous in data-driven spatiotemporal modeling for retrieving ocean MLD at the global scale, and significantly improves the estimation accuracy of MLD from remote sensing observations.
Keyword ：
global ocean global ocean Mixed layer depth Mixed layer depth remote sensing observations remote sensing observations residual convolutional gate recurrent unit residual convolutional gate recurrent unit
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| GB/T 7714 | Su, Hua , Tang, Zhiwei , Qiu, Junlong et al. Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2024 , 17 (1) . |
| MLA | Su, Hua et al. "Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 17 . 1 (2024) . |
| APA | Su, Hua , Tang, Zhiwei , Qiu, Junlong , Wang, An , Yan, Xiao-Hai . Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2024 , 17 (1) . |
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