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学者姓名：苏华
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Abstract ：
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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3-D ocean temperature and salinity data are the basis for studying ocean dynamic processes and warming. Satellite remote sensing observations on the ocean surface are abundant and full-coverage, while in situ observations in the ocean interior are very sparse and unevenly distributed. Currently, the remote sensing inversion models of temperature and salinity in the ocean interior are unable to learn both global and local detail information, and modeling layer-by-layer blocks the connection between vertical depth levels, resulting in poor accuracy. In this study, we proposed a novel clustering-guided and knowledge-distillation network (CGKDN) model based on the ocean knowledge-driven model. The model introduced K-means clustering for the partitions of ocean processes, knowledge distillation (KD) fusing global and local detail information, and adaptive depth gradient loss linking the vertical depth dimension, which enhanced the interpretability and accuracy of the model. Comparison of the reconstructions with the existing major publicly available datasets through the validation of 10% EN4 in situ profile observations from 2001 to 2020 reveals that the reconstructions are more accurate. Concretely, the average root mean square error (RMSE) (degrees C) across time-series and vertical levels of CGKDN/Institute of Atmospheric Physics (IAP)/OCEAN5 ocean analysis-reanalysis (ORAS5)/deep ocean remote sensing (DORS) ocean subsurface temperature (OST) is 0.590/0.598/0.690/0.723, and the average RMSE (PSU) of CGKDN/IAP/ORAS5 ocean subsurface salinity (OSS) is 0.101/0.103/0.106, respectively. Furthermore, the downscaled quarter-degree reconstructions present more mesoscale detail signals, consistent with the ARMOR3D data. This study not only improves the estimation accuracy of subsurface temperature and salinity but also serves the study of ocean interior dynamic processes and variabilities and provides valuable references for reconstructing other ocean subsurface physical variables.
Keyword ：
3-D reconstruction 3-D reconstruction Accuracy Accuracy Adaptation models Adaptation models Computational modeling Computational modeling global ocean global ocean Oceans Oceans Ocean temperature Ocean temperature Predictive models Predictive models Remote sensing Remote sensing remote sensing observations remote sensing observations Salinity (geophysical) Salinity (geophysical) Satellites Satellites subsurface thermohaline subsurface thermohaline Temperature sensors Temperature sensors
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| GB/T 7714 | Wang, An , Su, Hua , Huang, Zhanchao et al. Knowledge-Informed Deep Learning Model for Subsurface Thermohaline Reconstruction From Satellite Observations [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Wang, An et al. "Knowledge-Informed Deep Learning Model for Subsurface Thermohaline Reconstruction From Satellite Observations" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Wang, An , Su, Hua , Huang, Zhanchao , Yan, Xiao-Hai . Knowledge-Informed Deep Learning Model for Subsurface Thermohaline Reconstruction From Satellite Observations . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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The recognition of sea ice is of great significance for reflecting climate change and ensuring the safety of ship navigation. Recently, many deep-learning-based methods have been proposed and applied to segment and recognize sea ice regions. However, there are huge differences in sea ice size and irregular edge profiles, which bring challenges to the existing sea ice recognition. In this article, a global-local Transformer network, called SeaIceNet, is proposed for sea ice recognition in optical remote sensing images. In SeaIceNet, a dual global-attention head (DGAH) is proposed to capture global information. On this basis, a global-local feature fusion (GLFF) mechanism is designed to fuse global structural correlation features and local spatial detail features. Furthermore, a detail-guided decoder is developed to retain more high-resolution detail information during feature reconstruction for improving the performance of sea ice recognition. Extensive experiments on several sea ice datasets demonstrated that the proposed SeaIceNet has better performance than the existing methods in multiple evaluation indicators. Moreover, it excels in addressing challenges associated with sea ice recognition in optical remote sensing images, including the difficulty in accurately identifying irregular frozen ponds in complex environments, the broken and unclear boundaries between sea and thin ice that hinder precise segmentation, and the loss of high-resolution spatial details during model learning that complicates refinement.
Keyword ：
Accuracy Accuracy Climate change Climate change Data mining Data mining Deep learning Deep learning Feature extraction Feature extraction Ice Ice Image segmentation Image segmentation Integrated optics Integrated optics Optical imaging Optical imaging Optical sensors Optical sensors Remote sensing Remote sensing Sea ice Sea ice sea ice recognition sea ice recognition semantic segmentation semantic segmentation Transformer model Transformer model
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| GB/T 7714 | Hong, Wenjun , Huang, Zhanchao , Wang, An et al. SeaIceNet: Sea Ice Recognition via Global-Local Transformer in Optical Remote Sensing Images [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Hong, Wenjun et al. "SeaIceNet: Sea Ice Recognition via Global-Local Transformer in Optical Remote Sensing Images" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Hong, Wenjun , Huang, Zhanchao , Wang, An , Liu, Yuxin , Cai, Junchao , Su, Hua . SeaIceNet: Sea Ice Recognition via Global-Local Transformer in Optical Remote Sensing Images . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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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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Tidal flats in northern China are essential parts of the East Asian-Australasian Flyway, the densest pathway for migratory waterbirds, and are of great ecological and economic importance. They are threatened by human activities and climate change, raising the urgency surrounding tracking the spatiotemporal dynamics of tidal flats. However, there is no cost-effective way to map morphological changes on a large spatial scale due to the inaccessibility of the mudflats. In this study, we proposed a pixel-based multi-indices tidal flat mapping algorithm that precisely characterizes 2D/3D morphological changes in tidal flats in northern China using time-series remote sensing data. An overall accuracy of 0.95 in delineating tidal flats to a 2D extent was achieved, with 11,716 verification points. Our results demonstrate that the reduction in sediment discharge from rivers along the coastlines of the Yellow and Bohai Seas has resulted in an overall decline in the area of tidal flats, from 4856.40 km2 to 4778.32 km2. Specifically, 3D analysis showed that significant losses were observed in the mid-to-high-tidal flat zones, while low-elevation tidal flats experienced an increase in area due to the transformations in mid-to-high-tidal flats. Our results indicate that the sediment inputs from rivers and the succession of native vegetation are the primary drivers leading to 2D/3D morphological changes of tidal flats following the cessation of extensive land reclamation in northern China.
Keyword ：
2D/3D morphological changes 2D/3D morphological changes remote sensing remote sensing tidal flats tidal flats time series time series Yellow and Bohai Seas Yellow and Bohai Seas
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| GB/T 7714 | Gan, Zhiquan , Guo, Shurong , Chen, Chunpeng et al. Tracking the 2D/3D Morphological Changes of Tidal Flats Using Time Series Remote Sensing Data in Northern China [J]. | REMOTE SENSING , 2024 , 16 (5) . |
| MLA | Gan, Zhiquan et al. "Tracking the 2D/3D Morphological Changes of Tidal Flats Using Time Series Remote Sensing Data in Northern China" . | REMOTE SENSING 16 . 5 (2024) . |
| APA | Gan, Zhiquan , Guo, Shurong , Chen, Chunpeng , Zheng, Hanjie , Hu, Yuekai , Su, Hua et al. Tracking the 2D/3D Morphological Changes of Tidal Flats Using Time Series Remote Sensing Data in Northern China . | REMOTE SENSING , 2024 , 16 (5) . |
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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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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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叶绿素a浓度可以评估海水富营养化状况，对沿海叶绿素a浓度影响因素的研究在海洋环境保护方面具有重要意义。而现有研究多关注自然因素对沿海叶绿素a浓度的影响，忽视了人为因素的作用。因此实验以夜间灯光遥感数据表征人类活动强度，根据夜间灯光亮度和沿海叶绿素a浓度间的关系将东南沿海的城市分为3个类型，并同时结合海表温度、风速、太阳辐射、降水等自然因素，通过广义相加模型（GAM）分析不同季节下3类城市中人为和自然等多重因素对沿海叶绿素a浓度的影响。结果表明：在北海、汕头等类型Ⅰ城市中自然因素主导叶绿素a浓度的变化，春季的主导因素为风速，夏、秋、冬季为海表温度；而人类活动对叶绿素a浓度的影响较小且没有显著的影响关系。珠海、东莞等类型Ⅱ城市的叶绿素a浓度受自然因素主导，春、秋、冬季的主导因素为风速，夏季为海表温度；而人类活动在夏、秋季对沿海叶绿素a浓度有较大的促进作用。深圳、香港等类型Ⅲ城市中人为因素主导叶绿素a浓度的变化，春、夏、秋季人类活动对叶绿素a浓度的影响最大且为负相关，冬季海表温度对叶绿素a浓度的影响最大。
Keyword ：
东南沿海 东南沿海 人类活动 人类活动 叶绿素a 叶绿素a 广义相加模型(GAM) 广义相加模型(GAM) 自然因素 自然因素
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| GB/T 7714 | 张婧薇 , 陈佐旗 , 苏华 . 基于广义相加模型的东南沿海叶绿素a浓度的多重影响与季节差异 [J]. | 遥感技术与应用 , 2024 , 39 (01) : 134-148 . |
| MLA | 张婧薇 et al. "基于广义相加模型的东南沿海叶绿素a浓度的多重影响与季节差异" . | 遥感技术与应用 39 . 01 (2024) : 134-148 . |
| APA | 张婧薇 , 陈佐旗 , 苏华 . 基于广义相加模型的东南沿海叶绿素a浓度的多重影响与季节差异 . | 遥感技术与应用 , 2024 , 39 (01) , 134-148 . |
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Remote sensing scene classification (RSSC) is essential in Earth observation, with applications in land use, environmental status, urban development, and disaster risk assessment. However, redundant background interference, varying feature scales, and high interclass similarity in remote sensing images present significant challenges for RSSC. To address these challenges, this article proposes a novel hierarchical graph-enhanced transformer network (HGTNet) for RSSC. Initially, we introduce a dual attention (DA) module, which extracts key feature information from both the channel and spatial domains, effectively suppressing background noise. Subsequently, we meticulously design a three-stage hierarchical transformer extractor, incorporating a DA module at the bottleneck of each stage to facilitate information exchange between different stages, in conjunction with the Swin transformer block to capture multiscale global visual information. Moreover, we develop a fine-grained graph neural network extractor that constructs the spatial topological relationships of pixel-level scene images, thereby aiding in the discrimination of similar complex scene categories. Finally, the visual features and spatial structural features are fully integrated and input into the classifier by employing skip connections. HGTNet achieves classification accuracies of 98.47%, 95.75%, and 96.33% on the aerial image, NWPU-RESISC45, and OPTIMAL-31 datasets, respectively, demonstrating superior performance compared to other state-of-the-art models. Extensive experimental results indicate that our proposed method effectively learns critical multiscale visual features and distinguishes between similar complex scenes, thereby significantly enhancing the accuracy of RSSC.
Keyword ：
Attention mechanism Attention mechanism Attention mechanisms Attention mechanisms Data mining Data mining Earth Earth Feature extraction Feature extraction graph neural network (GNN) graph neural network (GNN) Graph neural networks Graph neural networks Remote sensing Remote sensing remote sensing scene classification (RSSC) remote sensing scene classification (RSSC) Scene classification Scene classification Sensors Sensors spatial structural feature spatial structural feature transformer transformer Transformers Transformers Visualization Visualization
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| GB/T 7714 | Li, Ziwei , Xu, Weiming , Yang, Shiyu et al. A Hierarchical Graph-Enhanced Transformer Network for Remote Sensing Scene Classification [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2024 , 17 : 20315-20330 . |
| MLA | Li, Ziwei et al. "A Hierarchical Graph-Enhanced Transformer Network for Remote Sensing Scene Classification" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17 (2024) : 20315-20330 . |
| APA | Li, Ziwei , Xu, Weiming , Yang, Shiyu , Wang, Juan , Su, Hua , Huang, Zhanchao et al. A Hierarchical Graph-Enhanced Transformer Network for Remote Sensing Scene Classification . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2024 , 17 , 20315-20330 . |
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