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学者姓名：黄展超
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Abstract ：
In large-scale disaster events, the planning of optimal rescue routes depends on the object detection ability at the disaster scene, with one of the main challenges being the presence of dense and occluded objects. Existing methods, which are typically based on the RGB modality, struggle to distinguish targets with similar colors and textures in crowded environments and are unable to identify obscured objects. To this end, we first construct two multimodal dense and occlusion vehicle detection datasets for large-scale events, utilizing RGB and height map modalities. Based on these datasets, we propose a multimodal collaboration network (MuDet) for dense and occluded vehicle detection, MuDet for short. MuDet hierarchically enhances the completeness of discriminable information within and across modalities and differentiates between simple and complex samples. MuDet includes three main modules: Unimodal Feature Hierarchical Enhancement (Uni-Enh), Multimodal Cross Learning (Mul-Lea), and Hard-easy Discriminative (He-Dis) Pattern. Uni-Enh and Mul-Lea enhance the features within each modality and facilitate the cross-integration of features from two heterogeneous modalities. He-Dis effectively separates densely occluded vehicle targets with significant intra-class differences and minimal inter-class differences by defining and thresholding confidence values, thereby suppressing the complex background. Experimental results on two re-labeled multimodal benchmark datasets, the 4K Stereo Aerial Imagery of a Large Camping Site (4K-SAI-LCS) dataset, and the ISPRS Potsdam dataset, demonstrate the robustness and generalization of the MuDet.
Keyword ：
Convolutional neural networks Convolutional neural networks Convolutional neural networks (CNNs) Convolutional neural networks (CNNs) dense and occluded dense and occluded Disasters Disasters Feature extraction Feature extraction hard-easy balanced attention hard-easy balanced attention large-scale disaster events large-scale disaster events multimodal vehicle detection (MVD) multimodal vehicle detection (MVD) Object detection Object detection Remote sensing Remote sensing remote Sensing (RS) remote Sensing (RS) Streaming media Streaming media Vehicle detection Vehicle detection
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| GB/T 7714 | Wu, Xin , Huang, Zhanchao , Wang, Li et al. Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Wu, Xin et al. "Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Wu, Xin , Huang, Zhanchao , Wang, Li , Chanussot, Jocelyn , Tian, Jiaojiao . Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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Abstract ：
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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Arbitrary-oriented object detection (AOOD) has been widely applied to locate and classify objects with diverse orientations in remote sensing images. However, the inconsistent features for the localization and classification tasks in AOOD models may lead to ambiguity and low-quality object predictions, which constrains the detection performance. In this article, an AOOD method called task-wise sampling convolutions (TS-Conv) is proposed. TS-Conv adaptively samples task-wise features from respective sensitive regions and maps these features together in alignment to guide a dynamic label assignment for better predictions. Specifically, sampling positions of the localization convolution in TS-Conv are supervised by the oriented bounding box (OBB) prediction associated with spatial coordinates, while sampling positions and convolutional kernel of the classification convolution are designed to be adaptively adjusted according to different orientations for improving the orientation robustness of features. Furthermore, a dynamic task-consistent-aware label assignment (DTLA) strategy is developed to select optimal candidate positions and assign labels dynamically according to ranked task-aware scores obtained from TS-Conv. Extensive experiments on several public datasets covering multiple scenes, multimodal images, and multiple categories of objects demonstrate the effectiveness, scalability, and superior performance of the proposed TS-Conv.
Keyword ：
Arbitrary-oriented object detection (AOOD) Arbitrary-oriented object detection (AOOD) convolutional neural network (CNN) convolutional neural network (CNN) Convolutional neural networks Convolutional neural networks dynamic label assignment dynamic label assignment Feature extraction Feature extraction Location awareness Location awareness Object detection Object detection oriented bounding box (OBB) oriented bounding box (OBB) Remote sensing Remote sensing Task analysis Task analysis task-wise sampling strategy task-wise sampling strategy Training Training
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| GB/T 7714 | Huang, Zhanchao , Li, Wei , Xia, Xiang-Gen et al. Task-Wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2024 . |
| MLA | Huang, Zhanchao et al. "Task-Wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2024) . |
| APA | Huang, Zhanchao , Li, Wei , Xia, Xiang-Gen , Wang, Hao , Tao, Ran . Task-Wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2024 . |
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Estimating high-resolution ocean subsurface temperature has great importance for the refined study of ocean climate variability and change. However, the insufficient resolution and accuracy of subsurface temperature data greatly limits our comprehensive understanding of mesoscale and other fine-scale ocean processes. In this study, we integrated multiple remote sensing data and in situ observations to compare four models within two frameworks (gradient boosting and deep learning). The optimal model, Deep Forest, was selected to generate a high-resolution subsurface temperature dataset (DORS0.25°) for the upper 2000 m from 1993 to 2023. DORS0.25° exhibits excellent reconstruction accuracy, with an average R2 of 0.980 and RMSE of 0.579 °C, and the monthly average accuracy is higher than IAP and ORAS5 datasets. Particularly, DORS0.25° can effectively capture detailed ocean warming characteristics in complex dynamic regions such as the Gulf Stream and the Kuroshio Extension, facilitating the study of mesoscale processes and warming within the global-scale ocean. Moreover, the research highlights that the rate of warming over the past decade has been significant, and ocean warming has consistently reached new highs since 2019. This study has demonstrated that DORS0.25° is a crucial dataset for understanding and monitoring the spatiotemporal characteristics and processes of global ocean warming, providing valuable data support for the sustainable development of the marine environment and climate change actions. © 2024 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)
Keyword ：
Climate change Climate change
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| GB/T 7714 | Su, Hua , Zhang, Feiyan , Teng, Jianchen et al. Reconstructing high-resolution subsurface temperature of the global ocean using deep forest with combined remote sensing and in situ observations [J]. | ISPRS Journal of Photogrammetry and Remote Sensing , 2024 , 218 : 389-404 . |
| MLA | Su, Hua et al. "Reconstructing high-resolution subsurface temperature of the global ocean using deep forest with combined remote sensing and in situ observations" . | ISPRS Journal of Photogrammetry and Remote Sensing 218 (2024) : 389-404 . |
| APA | Su, Hua , Zhang, Feiyan , Teng, Jianchen , Wang, An , Huang, Zhanchao . Reconstructing high-resolution subsurface temperature of the global ocean using deep forest with combined remote sensing and in situ observations . | ISPRS Journal of Photogrammetry and Remote Sensing , 2024 , 218 , 389-404 . |
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