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IEEE International Symposium on Biomedical Imaging

作者： Mingyue Zhao Xiaolan Qiu S.Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China (USTC) China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Airway extraction is paramount in the early diagnosis and treatment of respiratory diseases. As a tree-like structure, both topological-aware learning and voxel-wise classification are equally crucial for the airway. However, existing methods demonstrate insufficient topological learning, emphasizing only the supervision of individual key topological points. Consequently, this paper proposes a Explicit Topological Modeling (ExpTopo) approach to aid airway segmentation. It explicitly introduces topological metric space learning based on semantic segmentation, enhancing the model's structural perception by implementing global skeleton-level sparse topological learning (STL) and local voxel-level dense topological perception (DTP). Extensive experimental results demonstrate that the algorithm achieves competitive performance at both the topological and voxel levels. Code will be available in https://***/MorineZ/ExpTopo.

关键词： Codes Atmospheric modeling Semantic segmentation Pulmonary diseases Biological system modeling Extraterrestrial measurements Biomedical imaging

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Biophysical impact of forest age changes on land surface temperature in China

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Science of the Total Environment 2025年 964卷 178445页

作者： Zhang, Zhijiang Wang, Lunche Chen, Chao Zhang, Xiang Ding, Chao Yuan, Moxi Shen, Lixing Li, Xinxin School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou215009 China Suzhou Key Laboratory of Spatial Information Intelligent Technology and Application Suzhou University of Science and Technology Suzhou215009 China School of Geography and Information Engineering China University of Geosciences Wuhan430074 China Department of Geographic Science Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China School of Public Administration and Human Geography Hunan University of Technology and Business Changsha410205 China School of Atmospheric Science and Remote Sensing Wuxi University Wuxi214105 China School of Resource and Environmental Science Wuhan University Wuhan430079 China

Forest age structures have been substantially affected by natural disturbances and anthropogenic activities worldwide. Their changes can significantly influence local and nonlocal climate through both the biogeochemical and biophysical processes. However, numerous studies have focused on the biogeochemical effect of forest age changes whereas the biophysical effect has received far less attention. Here we investigated how forest age changes influence land surface temperature (LST) by comparing older forests and adjacent younger forests pixels and unraveled underlying biophysical mechanisms using satellite observations over China during 2003–2012. Our study showed that older forests had a substantial annual cooling benefit than adjacent medium-aged and young forests. Attribution analysis indicated that the cooling effect of latent heat flux counteracted the albedo-induced warming effect, leading to the net cooling effect of older evergreen needle-leaved forest or evergreen broadleaved forest. Furthermore, the cooling effect of sensible heat flux is greater than the albedo-driven warming effect, contributing to the net cooling effect of older deciduous broadleaved forest. Our work is a step forward to underscore the potential of preserving mature forests as a local climate adaptation strategy and provides important parameterization foundation for earth system models without incorporation of forest age modules. © 2025 Elsevier B.V.

关键词： Land surface temperature

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Synthetic aperture radar inshore ship detection based on multivariate feature fusion

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Journal of Electronic Imaging 2025年第2期34卷

作者： Yan, Junhua Sang, Di Gao, Yinsen Liang, Shuo Zhang, Yin Wang, Dawei Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Space Photoelectric Detection and Perception Nanjing China Joint Laboratory of Spatial Intelligent Perception and Large Model Application Meishan China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China

We propose an algorithm for detecting inshore ship targets in synthetic aperture radar (SAR) images, named MFF-YOLO (You Only Look Once of multivariate feature fusion). On the basis of YOLOv5, the weakly supervised saliency feature module is constructed to enhance the feature texture under the supervision of weakly supervised labels, extract the multi-scale saliency target features, solve the problem that it is difficult to locate potential target regions in the SAR image with insufficient inshore target features, and improve the recall rate by 3.2%;the land-sea feature module is constructed to extract the land-sea distribution features of the image, suppress the land area features, solve the problem of high false alarm rate caused by land clutter in inshore scenes, and improve the precision rate by 3.2%;construct multivariate feature fusion module to fuse the target features, weakly supervised saliency features and land-sea distribution features in multiple scales;and use adaptive thresholding to suppress irrelevant noise to obtain target features enhanced by multivariate features fusion, which solves the problems of insufficient target available information and irrelevant noise interference in SAR images, improves AP0.5 by 3.3%, and improves F1 measure by 2%. This algorithm achieves a precision of 88.3% on the inshore portion of the high-resolution SAR image dataset, a recall of 77.5%, an AP0.5 of 81.6%, and an F1 of 0.825, demonstrating superior detection performance over other comparative algorithms, with good accuracy and robustness. © 2025 SPIE. All rights reserved.

关键词： inshore multivariate features fusion saliency ship detection synthetic aperture radar images

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Adaptive disentangled target representation for unsupervised domain adaptation in remote sensing segmentation

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Engineering applications of Artificial Intelligence 2025年 156卷

作者： Lu, Runuo Dong, Shoubin Jia, Jianxin Wang, Xusheng Liu, Kai Chen, Jinsong Guo, Shanxin Zheng, Xiaorou Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou510641 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute EspooFI-02150 Finland Center for Geo-Spatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application Shenzhen518055 China

Due to the significant differences between the source and target domains, semantic segmentation models for remote sensing images trained on the source domain often struggle to generalize effectively to new target domains, leading to performance degradation. Most solutions disentangle features into domain-invariant features shared by both domains and domain-specific features unique to each domain, relying primarily on the domain-invariant features for model construction and inference. However, during this process, critical domain-specific features as essential information are often discarded as noise, leading to a reduction in segmentation accuracy. To address these issues, this study proposes an unsupervised domain adaptation, called ChameleonRS. ChameleonRS separates the domain-invariant features and the domain-specific features by disentanglement learning, and introduces a multi-level feature alignment module to enhance the representation of domain-invariant features. More importantly, it aligns predictions based on domain-invariant features with those based on fused invariant and specific features using contrastive learning, effectively capturing target-specific features. This study explores two cross-domain semantic segmentation tasks: (I) Geographic location variation. (II) Geographic location and imaging mode variation. Results show that under setting (I), ChameleonRS outperforms the state-of-the-art method by 4.79% in Intersection over Union (IoU) and 6.51% in the F1 score. Under setting (II), it achieves improvements of 4.46% and 4.82% in IoU and F1 score. Results demonstrate the superiority and adaptability of ChameleonRS under varying geographical and imaging conditions, effectively mitigating the generalization issues caused by distribution differences, and thereby promoting the development and application of remote sensing technologies in practical scenarios. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

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Stacking, Strain-Engineering Induced Altermagnetism, Multipiezo Effect, and Topological State in Two-Dimensional Materials

arXiv

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arXiv 2025年

作者： Xun, Wei Liu, Xin Zhang, Youdong Wu, Yin-Zhong Li, Ping Jiangsu Engineering Research Center for Design and Application of Intelligent Detection Equipment School of Electronic Engineering Jiangsu Vocational College of Electronics and Information Huaian223003 China School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China State Key Laboratory for Mechanical Behavior of Materials Center for Spintronics and Quantum System School of Materials Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China State Key Laboratory for Surface Physics Department of Physics Fudan University Shanghai200433 China State Key Laboratory of Silicon and Advanced Semiconductor Materials Zhejiang University Hangzhou310027 China

Altermagnetism, as a newly identified form of unconventional antiferromagnetism, enables the removal of spin degeneracy in the absence of net magnetization that provides a platform for the low power consumption and ultra-fast device applications. However, the rare attention has been paid to the relationship between stacking, strain and altermagnet, multipiezo effect and topological state. Here, we propose a mechanism to realize the altermagnet, multipiezo effect, and topological state in two-dimensional materials by the stacking and strain engineering. Based on the analysis of symmetry, we find that the spin splitting feature related to the Ut, PTt, MzUt, or MzPTt symmetries in altermagnet multilayers. In addition, we find that the stacking engineering can effectively realize the transform from antiferromagnetism to altermagnetism and semiconductor to metal for the Jauns bilayer V2SeTeO. More interestingly, the strain not only induces an intriguing multipiezo effect, encompassing the piezovalley, piezomagnetism and piezoelectric, but also achieves the abundant topological phase. Our findings offer a generalized direction for manipulating the spin splitting, valley polarization, and topological states, promoting practical application of valleytronic and spintronic devices based on two-dimensional altermagnets. © 2025, CC BY.

关键词： Antiferromagnetism

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Active transfer learning of matching query results across multiple sources

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Frontiers of Computer Science 2015年第4期9卷 595-607页

作者： Jie XIN Zhiming CUI Pengpeng ZHAO Tianxu HE The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China

Entity resolution （ER） is the problem of identi- fying and grouping different manifestations of the same real world object. Algorithmic approaches have been developed where most tasks offer superior performance under super- vised learning. However, the prohibitive cost of labeling training data is still a huge obstacle for detecting duplicate query records from online sources. Furthermore, the unique combinations of noisy data with missing elements make ER tasks more challenging. To address this, transfer learning has been adopted to adaptively share learned common structures of similarity scoring problems between multiple sources. Al- though such techniques reduce the labeling cost so that it is linear with respect to the number of sources, its random sam- piing strategy is not successful enough to handle the ordinary sample imbalance problem. In this paper, we present a novel multi-source active transfer learning framework to jointly select fewer data instances from all sources to train classi- fiers with constant precision/recall. The intuition behind our approach is to actively label the most informative samples while adaptively transferring collective knowledge between sources. In this way, the classifiers that are learned can be both label-economical and flexible even for imbalanced or quality diverse sources. We compare our method with the state-of-the-art approaches on real-word datasets. Our exper- imental results demonstrate that our active transfer learning algorithm can achieve impressive performance with far fewerlabeled samples for record matching with numerous and var- ied sources.

关键词： entity resolution active learning transfer learn-ing convex optimization

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Improved S2ANet based on attention mechanism for small target detection in remote sensing images

Improved S2ANet based on attention mechanism for small targe...

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2021 CIE International Conference on Radar, Radar 2021

作者： Lu, Dongdong Tie, Wenjie Lei, Songlin Qiu, Xiaolan Suzhou Aerospace Information Research Institute Laboratory of Spatial Information Intelligent Processing System Suzhou China Chinese Academy of Sciences Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Beijing China

ISBN: (纸本)9781665498142

Deep learning has achieved remarkable results in the field of target detection and recognition. For small targets in images, image pyramid can be used to fuse multi-scale features to improve detection performance. However, when test on remote sensing images, it is found that some important small goals will still be missed. Aiming at the problems of small target detection in remote sensing images, this paper proposes two S2ANet improved network variants based on the attention mechanism for synthetic aperture radar(SAR) payload and optical payload: for SAR images, CBAM(Convolutional Block Attention Module) attention is added to the backbone network to increase the attention of the feature extraction network for small targets in the channel and space dimensions;For optical images, firstly increase the CBAM channel attention and spatial attention in the backbone, secondly, the SE(Squeeze And Excitation) channel attention is added to the input features before the channel dimension fusion of the pyramid structure, then the attention-enhanced targets are fully integrated through up-sampling and horizontal splicing operations to improve the missed alarm rate and recall rate of small remote sensing targets. Finally, a comparative verification was carried out on the constructed airborne KU-band SAR data set and the optical remote sensing aircraft data set composed of some DOTA1.5 and FAIR1M data sets. The experimental results proved that the designed two variant networks can be used for small remote sensing. There have been certain improvements in the missed alarm rate, accuracy rate, and recall rate of the target. © 2021 IEEE.

关键词： Radar remote sensing Radar detection Object detection Radar imaging Optical imaging Feature extraction Adaptive optics

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Preliminary Analysis of PolInSAR Scattering Mechanism in Urban Areas through UAV-borne PolInSAR System

Preliminary Analysis of PolInSAR Scattering Mechanism in Urb...

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2021 CIE International Conference on Radar, Radar 2021

作者： Lv, Zexin Qiu, Xiaolan Ding, Chibiao Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing China Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Aerospace Big Dara Application Technology Suzhou China Suzhou Aerospace Information Research Institute Suzhou China

ISBN: (纸本)9781665498142

Polarimetric Interferometric SAR (PolInSAR) can improve the coherence of images and it plays an important role in urban remote sensing. The explanation of its scattering mechanism is concerned by many researchers. It is always explained from the aspect of different polarization decompositions but seldom from the height of scattering centers. In this paper, a Ku-band UAV-borne PolInSAR system is used to compare the relationship of scattering centers between PolInSAR and Pauli decomposition on different artificial targets. The physical meaning of PolInSAR is preliminary analyzed and our conclusion is also verified through a C-band manned airborne PolInSAR system. © 2021 IEEE.

关键词： Pauli decomposition Polarimetric Interferometric SAR (PolInSAR) scattering center Unmanned Aerial Vehicle (UAV)

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HVAC Optimal Control Based on the Sensitivity Analysis:An Improved SA Combination Method Based on a Neural Network

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2741-2758页

作者： Lifan Zhao Zetian Huang Qiming Fu Nengwei Fang Bin Xing Jianping Chen School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China Suzhou Key Laboratory of Mobile Network Technology and Application Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation Center Co. Ltd.Chongqing400707China

Aiming at optimizing the energy consumption of HVAC,an energy conservation optimization method was proposed for HVAC systems based on the sensitivity analysis(SA),named the sensitivity analysis combination method(SAC).Based on the SA,neural network and the related settings about energy conservation of HVAC systems,such as cooling water temperature,chilled water temperature and supply air temperature,were ***,based on the data of the existing HVAC system,various optimal control methods ofHVAC systems were tested and evaluated by a simulated HVAC system in *** results show that the proposed SA combination method can reduce significant computational load while maintaining an equivalent energy performance compared with traditional optimal control methods.

关键词： Energy conservation sensitivity analysis HVAC system neural network

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HPHR-SAR-Net: Hyperpixel High-Resolution SAR Imaging Network Based on Nonlocal Total Variation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2023年 16卷 8595-8608页

作者： Zhou, Guoru Xu, Zhongqiu Fan, Yizhe Zhang, Zhe Qiu, Xiaolan Zhang, Bingchen Fu, Kun Wu, Yirong Chinese Academy of Sciences Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing100045 China University of Chinese Academy of Sciences Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100045 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou215009 China

High resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. First, these algorithms tend to focus on local information, neglecting nonlocal information between different pixel patches. Second, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Third, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a hyperpixel high-resolution SAR imaging network (HPHR-SAR-Net) for rapid despeckling in high-resolution modes. Based on the concept of hyperpixel techniques, we initially combine nonconvex and nonlocal total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the alternating direction method of multipliers (ADMM) algorithm and unfold it into a deep unfolding network (DUN). The network's parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the proposed HPHR-SAR-Net is compatible with high-resolution imaging modes, such as spotlight, staring spotlight, and sliding spotlight. In this article, we demonstrate the superiority of HPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that HPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results. © 2008-2012 IEEE.

关键词： Radar imaging

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