检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

SPHR-SAR-Net: Superpixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation

arXiv

引用

arXiv 2023年

作者： Zhou, Guoru Xu, Zhongqiu Fan, Yizhe Zhang, Zhe Qiu, Xiaolan Zhang, Bingchen Fu, Kun Wu, Yirong The Key Laboratory of Technology in Geo-spatial Information Processing Application System Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing 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. Firstly, these algorithms tend to focus on local information, neglecting non-local information between different pixel patches. Secondly, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Thirdly, 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 Superpixel High-Resolution SAR Imaging Network (SPHR-SAR-Net) for rapid despeckling in high-resolution SAR mode. Based on the concept of superpixel techniques, we initially combine nonconvex and non-local 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 Unfolded Network (DUN). The network’s parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the Deep Unfolded Network is compatible with high-resolution imaging modes such as spotlight, staring spotlight, and sliding spotlight. In this paper, we demonstrate the superiority of SPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that SPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results. Copyright © 2023, The Authors. All rights reserved.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

A Novel Gradient Descent Least Squares (GDLS) Algorithm for Efficient SMV Gridless Line Spectrum Estimation with applications in Tomographic SAR Imaging

arXiv

引用

arXiv 2022年

作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Jiangsu Suzhou215123 China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing100190 China

This paper presents a novel efficient method for gridless line spectrum estimation problem with single snapshot, namely the gradient descent least squares (GDLS) method. Conventional single snapshot (a.k.a. single measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the array aperture, or adopt the sparsity constraint and utilize compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least squares (LS) estimation problem and solves the corresponding objective function via gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, as well as performance analysis are discussed in this paper. Numerical simulations and real data experiments show that the proposed GDLS algorithm outperforms the state-of-the-art methods e.g., CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experiment data. Results show great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectrum analysis

来源：评论

学校读者我要写书评

暂无评论

A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography

arXiv

引用

arXiv 2024年

作者： Gao, Silin Wang, Wenlong Wang, Muhan Zhang, Zhe Yang, Zai Qiu, Xiaolan Zhang, Bingchen Wu, Yirong Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Mathematics & Statistics Xi’an Jiaotong University Xi’an710049 China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Suzhou215123 China China National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China

Synthetic aperture radar (SAR) tomography (TomoSAR) retrieves three-dimensional (3-D) information from multiple SAR images, effectively addresses the layover problem, and has become pivotal in urban mapping. Unmanned aerial vehicle (UAV) has gained popularity as a TomoSAR platform, offering distinct advantages such as the ability to achieve 3-D imaging in a single flight, cost-effectiveness, rapid deployment, and flexible trajectory planning. The evolution of compressed sensing (CS) has led to the widespread adoption of sparse reconstruction techniques in TomoSAR signal processing, with a focus on 1 norm regularization and other grid-based CS methods. However, the discretization of illuminated scene along elevation introduces modeling errors, resulting in reduced reconstruction accuracy, known as the "off-grid" effect. Recent advancements have introduced gridless CS algorithms to mitigate this issue. This paper presents an innovative gridless 3-D imaging framework tailored for UAV-borne TomoSAR. Capitalizing on the pulse repetition frequency (PRF) redundancy inherent in slow UAV platforms, a multiple measurement vectors (MMV) model is constructed to enhance noise immunity without compromising azimuth-range resolution. Given the sparsely placed array elements due to mounting platform constraints, an atomic norm soft thresholding algorithm is proposed for partially observed MMV, offering gridless reconstruction capability and super-resolution. An efficient alternative optimization algorithm is also employed to enhance computational efficiency. Validation of the proposed framework is achieved through computer simulations and flight experiments, affirming its efficacy in UAV-borne TomoSAR applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Superb Microplastics Separation Performance of Graphene Oxide Tuned by Laser Bombardment

SSRN

引用

SSRN 2023年

作者： Sun, Jiawei Xiong, Yuwei Bi, Hengchang Sun, Yunfei Yin, Kuibo Jia, Haiyang Han, Longxiang College of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China SEU-FEI Nano-Pico Center Key Laboratory of MEMS of Ministry of Education Southeast University Nanjing210096 China School of Physics and New Energy Xuzhou University of Technology Xuzhou221018 China Jiangsu Industrial Intelligent and Low-carbon Technology Engineering Center Suzhou215000 China Suzhou Key Laboratory of Intelligent Low-carbon Technology Application Suzhou215000 China

Microplastics (MPs) have been identified as a significant environmental threat to aquatic ecosystems and human health. Consequently, there is an urgent need for efficient separation methods for small-sized MPs. In this study, a super-hydrophilic GO membrane is successfully prepared by facilely depositing GO on a microfiltration substrate, without introducing any surface modification materials, especially nanoparticles, which may cause secondary pollution. Laser bombardment reduces the size of the GO lamella (23.6% of its original size) and creates an abundance of defects and undulating wrinkles, enabling the deposited GO membrane to have more and shorter pathways for water. As a result, the filtration flux for 10 μm polyvinyl chloride (PVC) reaches up to 3396 L m−2 h−1 bar−1, a 1~2-order-of-magnitude enhancement compared to the unirradiated GO membrane, and is also superior to most nanoparticle-modified GO membranes. Simultaneously, the labyrinth structure endows the membrane with a high filtration efficiency of approximately 99% for the majority of MPs. This excellent performance remains virtually unchanged after repeated use. The integration of outstanding separation effects and health safety presents opportunities for practical applications in long-term MP-in-water separation. © 2023, The Authors. All rights reserved.

关键词： Aquatic ecosystems

来源：评论

学校读者我要写书评

暂无评论

Multi-aspect Tomographic SAR Imaging Approach via Distributed Compressed Sensing and Joint Sparsity

Multi-aspect Tomographic SAR Imaging Approach via Distribute...

引用

2021 CIE International Conference on Radar, Radar 2021

作者： Du, Bang Zhang, Zhe Qiu, Xiaolan Lei, Bin 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 Aerospace Information Research Institute Chinese Academy of Sciences Suzhou China

ISBN: (纸本)9781665498142

Synthetic aperture radar (SAR) tomography (TomoSAR) is a novel technique that enables three-dimensional (3-D) imaging and plays an important role in urban remote sensing by utilizing multiple observations of the same target scene from various baselines. Canonical TomoSAR observations are from a single aspect, which has been well studies already. However, modern SAR sensors such as Unmanned Aerial Vehicle (UAV) allow us to achieve multi-aspect TomoSAR data of the same target scene. This paper proposes a novel framework named 'Multi-aspect TomoSAR,' which takes advantage of rich TomoSAR data from multiple observation aspects. We derive the multi-aspect TomoSAR signal model using distributed compressed sensing (DCS) and adopt a simultaneous sparse approximation algorithm named SOMP to solve the joint sparsity model. Numerical results on synthetic simulated data show that the multi-aspect estimation can provide more accurate estimation, yield a promising perspective. Experimental results on real airborne data will be reported in the journal version of this work later. © 2021 IEEE.

关键词： Distributed compressed sensing Joint sparsity model Multi-aspect TomoSAR

来源：评论

学校读者我要写书评

暂无评论

Electromagnetic Simulation Aided SAR Target Classification Via Deep Domain Adaptation

Electromagnetic Simulation Aided SAR Target Classification V...

引用

2021 CIE International Conference on Radar, Radar 2021

作者： Lv, Xiaoling Qiu, Xiaolan Yu, Wenming Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Suzhou Aerospace Information Research Institute Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Suzhou China Southeast University State Key Laboratory of Millimeter Waves Nanjing China

ISBN: (纸本)9781665498142

Convolutional neural networks (CNNs) have made tremendous success in optical images classification recently. However, in synthetic aperture radar (SAR) target classification, it is difficult to annotate a large amount of real SAR images to train CNNs. Sufficient annotated images can be easily obtained through simulation, but the disparity between the simulated images and the real images makes them difficult to directly apply to the real images classification. In this paper, we propose a model that integrates multi-kernel maximum mean discrepancy (MK-MMD) and domain-adversarial training to alleviate this problem. Simulated SAR images with annotation and unlabeled real SAR images are used to train our model. First, we use domain-adversarial training to prompt the model to extract domain-invariant features. Then, the MK-MMD between the hidden representations of simulated images and real images is reduced to narrow domain discrepancy. Experimental results on the real SAR dataset demonstrate that our method effectively solves the domain shift problem and improves the classification accuracy. © 2021 IEEE.

关键词： convolutional neural network (CNN) Synthetic aperture radar (SAR) target classification unsupervised domain adaptation (UDA)

来源：评论

学校读者我要写书评

暂无评论

Research on Zenith Tropospheric Delay Model Based on TCN Improving HGPT2 Model

Research on Zenith Tropospheric Delay Model Based on TCN Imp...

引用

第十四届中国卫星导航年会

作者： Dengao Li Danyang Shi Jumin Zhao Fanming Wu Liangquan Yan Ran Feng Xinfang Zhang Jinhua Zhao College of Data Science Taiyuan University of Technology Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Intelligent Perception Engineering Technology Center of Shanxi Shanxi Province Engineering Technology Research Center of Spatial Information Network College of Information and Computer Taiyuan University of Technology

The zenith tropospheric delay（ZTD） obtained by global navigation satellite system（GNSS） atmospheric sounding is a pivotal data source for water vapor monitoring. Meteorological changes in Antarctica play an important role in analysis of the global climate, but factors such as complex climatic conditions can limit the collection of meteorological data needed for ZTD retrieval. Therefore, it is necessary to establish high-precision ZTD models that do not rely on measured meteorological data. The existing global pressure and temperature 3（GPT3） model has limited ability to capture complex weather variations and cannot obtain high-precision GPT3TD. To address the above issues, this research proposes a high-precision ZTD model through using temporal convolutional network（TCN） for improving the hourly global pressure and temperature 2（HGPT2） model. The HGTP2 model based on Fourier analysis and the time-segmentation concept can consider the linear trend of climate variations, simultaneously the TCN is introduced to simulate the long-term temporal dependence of HGPT2TD, so as to achieve the goal of obtaining high-precision ZTD without relying on measured meteorological data. The experimental results show that the precision of TCNTD obtained from the proposed model is higher than GPT3TD and HGPT2TD when using the GNSSTD observations obtained from 35 stations in Antarctica as a reference, and the improvement of the model is obvious.

关键词： GNSS ZTD GPT3 HGPT2 TCN

来源：评论

学校读者我要写书评

暂无评论

Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM

Heavy Rainfall Prediction Model Using Sample Entropy Derived...

引用

第十四届中国卫星导航年会

作者： Fanming Wu Dengao Li Jinhua Zhao Ran Feng Danyang Shi Xinfang Zhang Jumin Zhao College of Data Science Taiyuan University of Technology Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Intelligent Perception Engineering Technology Center of Shanxi Shanxi Province Engineering Technology Research Center of Spatial Information Network College of Information and Computer Taiyuan University of Technology

There is a growing interest to use Global Navigation Satellite System(GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is *** sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile,combining GNSS-PWV time domain features(PWV value, PWV increment,PWV decrement, rate of PWV increment, rate of PWV decrement) and colocated meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong’s HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：