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SAR image water extraction using the attention U-net and multi-scale level set method:flood monitoring in South China in 2020 as a test case

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Geo-Spatial Information science 2022年第2期25卷 155-168页

作者： Chuan Xu Shanshan Zhang Bofei Zhao Chang Liu Haigang Sui Wei Yang Liye Mei School of Computer Science Hubei University of TechnologyWuhanChina Geographic Information Center GuangZhou Urban Planning&Design Survey Research InstituteGuangzhouChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Information Science and Engineering Wuchang Shouyi UniversityWuhanChina The Institute of Technological Sciences Wuhan UniversityWuhanChina

Level set method has been extensively used for image segmentation,which is a key technology of water ***,one of the problems of the level-set method is how to find the appropriate initial surface parameters,which will affect the accuracy and speed of level set ***,the semantic segmentation based on deep learning has opened the exciting research *** addition,the Convolutional Neural Network(CNN)has shown a strong feature representation ***,in this paper,the CNN method is used to obtain the initial SAR image segmentation map to provide deep a priori information for the zero-level set curve,which only needs to describe the general outline of the water body,rather than the accurate *** with the traditional circular and rectangular zero-level set initialization method,this method can converge to the edge of the water body faster and more precisely;it will not fall into the local minimum value and be able to obtain accurate segmentation *** effectiveness of the proposed method is demonstrated by the experimental results of flood disaster monitoring in South China in 2020.

关键词： Water extraction flood monitoring level set attention U-net Convolutional Neural Network(CNN)

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SPTU-Lite: An Efficient Auxiliary Diagnostic Approach Combining Spiking Neural Networks And Large Kernel Extractors For ECG Signals

SPTU-Lite: An Efficient Auxiliary Diagnostic Approach Combin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chuxuan Shan Xiaohua Wang Hang Qi Qingxu Meng Jiabao Wang Weijiang Wang Yueting Shi School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing China Science and Technology on Millimeter-Wave Laboratory Beijing Institute of Remote-Sensing Equipment Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Electrocardiogram (ECG) is essential for the early prevention and diagnosis of cardiovascular diseases. The rise of wearable ECG devices has facilitated high-precision heart rate detection, enabling early interventions. Advances in Artificial Neural Networks (ANNs), particularly One-Dimensional Convolutional Neural Networks (CNNs) and Transformers, have significantly improved ECG signal classification. However, their computational and parametric demands limit their application, especially in small-scale devices. Spiking Neural Networks (SNNs), which operate on an event-driven principle, offer a solution with their resource efficiency. In this study, we propose a U-shaped hybrid CNN-Transformer architecture combining Spiking Fully Connected (FC) Layers and large kernel extractor (LKE) modules, named Spiking TransU-Lite (SPTU-Lite). The LKE module enhances the network’s receptive field and improves parameter efficiency, reducing model size without significant precision loss. Additionally, spiking neurons replace the Fully Connected classification layer, further reducing the model’s complexity. Empirical results on the MIT-BIH dataset show that SPTU-Lite reduces parameters by approximately 95%, while maintaining an accuracy of 99.62% and a loss rate below 0.5%, demonstrating its potential for ECG analysis in resource-constrained environments.

关键词： Accuracy Heart beat Prevention and mitigation Neurons Pattern classification Spiking neural networks Electrocardiography Transformers Kernel Speech processing

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Robust H∞ Attitude Stabilization for the Cluster Takeover of Non-cooperative Flexible Spacecraft 1

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29th International Conference on Computational and Experimental Engineering and sciences, ICCES 2023

作者： Yue, Xiaokui Yang, Ziyu Yu, Hongyu Huang, Panxing Liu, Yuan National Key Laboratory of Aerospace Flight Dynamics School of Astronautics Northwestern Polytechnical University Xi’an710072 China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing100094 China Beijing Institute of Remote Sensing Information Beijing100011 China

ISBN: (数字)9783031449475

ISBN: (纸本)9783031449468

In this paper, a cooperative control scheme based on the small spacecraft cluster is proposed for the takeover control of the failed flexible spacecraft in space. In order to overcome external disturbance, inertia uncertainty and controller gain perturbation, the controller spacecraft calculates the control torque which can stabilize the combined spacecraft in theory, and generates the desired control signal according to the robust H∞ non-fragile control law set in advance. Based on the communication topology, the actuator spacecraft restores the control signal according to the distributed control torque allocation scheme considering the saturation state of all reaction wheels, and implements it to achieve the attitude stabilization and vibration suppression of the combined spacecraft. Simulation results show that the distributed control torque allocation scheme can implement the desired control signal with relatively small error. Under the condition that the external disturbance, controller gain perturbation and inertia uncertainty coexist, the angle and angular velocity of the combined spacecraft can be stabilized quickly. Moreover, the vibration of the flexible appendages can also be suppressed effectively. During the whole takeover control process, the dynamic allocation scheme can always ensure that all reaction wheels of each actuator spacecraft never reach saturation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Flexible spacecraft

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A Physics-Based Atmospheric Precipitable Water Vapor Retrieval Algorithm by Synchronizing Modis Near-Infrared and Thermal Infrared Measurements

SSRN

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SSRN 2024年

作者： Zhou, Shugui Cheng, Jie School of Geo-Science and Technology Zhengzhou University Zhengzhou450001 China Institute of Remote Sensing Science and Engineering Faculty of Geographical Science Beijing Normal University Beijing100875 China State Key Laboratory of Remote Sensing Science Jointly sponsored by Beijing Normal University Aerospace Information Research Institute Chinese Academy of Sciences Beijing100875 China

This study proposed an innovative joint inversion algorithm that synchronized Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) and thermal infrared (TIR) radiance data for accurate estimates of clear-sky precipitable water vapor (PWV). The NIR-TIR joint inversion algorithm consists of three core parts: (1) the NIR radiative transfer equation was simplified by assuming that reflectance changes approximately linearly with wavelength within the 0.85-1.25 μm range, which facilitates the simulation of top-of-atmosphere (TOA) radiance of the NIR water vapor absorption channel without requiring explicit reflectance information;(2) partial derivatives of NIR-TIR TOA radiance with respect to the corresponding background fields were derived by applying the one-term variational theorem to the radiative transfer equation;(3) an optimization approach was utilized to adjust the background fields until the discrepancy between the simulated and observed NIR-TIR TOA radiances was minimized. Subsequently, the refined water vapor profile was integrated to derive the PWV. In situ measurements collected from the Suominet and AERONET networks across North America were utilized to validate the NIR-TIR joint inversion algorithm. Additionally, the MODIS NIR PWV product (MYD05) and TIR PWV product (MYD07) were validated using the same in situ measurements for purposes of comparison. Validation results indicated the bias and root mean square error (RMSE) of the NIR-TIR joint inversion algorithm were -0.55 mm and 2.08 mm, respectively, which are superior to those of the MODIS NIR and TIR PWV products. The bias and RMSE were 3.84 mm and 4.86 mm for MYD05, and 0.41 mm and 4.60 mm for MYD07. Overall, the NIR-TIR joint inversion algorithm can disentangle temperature and water vapor information from TIR observation and achieve the accurate retrieval of the clear-sky PWV. This study offers a potential way to generate comprehensive, long-term, high spatial resolution environm

关键词： Water vapor

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TASK SPECIFIC PRETRAINING WITH NOISY LABELS FOR remote sensing IMAGE SEGMENTATION

arXiv

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arXiv 2024年

作者： Liu, Chenying Albrecht, Conrad M. Wang, Yi Zhu, Xiao Xiang Data Science in Earth Observation Technical University of Munich Germany Remote Sensing Technology Institute German Aerospace Center Germany Germany

Compared to supervised deep learning, self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations. While image-level information for unsupervised pretraining efficiently works for various classification downstream tasks, the performance on pixel-level semantic segmentation lags behind in terms of model accuracy. On the contrary, many easily available label sources (e.g., automatic labeling tools and land cover land use products) exist, which can provide a large amount of noisy labels for segmentation model training. In this work, we propose to exploit noisy semantic segmentation maps for model pretraining. Our experiments provide insights on robustness per network layer. The transfer learning settings test the cases when the pretrained encoders are fine-tuned for different label classes and decoders. The results from two datasets indicate the effectiveness of task-specific supervised pretraining with noisy labels. Our findings pave new avenues to improved model accuracy and novel pretraining strategies for efficient remote sensing image segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A labor-free index-guided semantic segmentation approach for urban vegetation mapping from high-resolution true color imagery

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International Journal of Digital Earth 2023年第1期16卷 1640-1660页

作者： Peng Zhang Cong Lin Shanchuan Guo Wei Zhang Hong Fang Peijun Du Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural ResourcesSchool of Geography and Ocean ScienceNanjing UniversityNanjingPeople’s Republic of China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application NanjingPeople’s Republic of China c Nanjing Research Institute of Surveying Mapping&Geotechnical InvestigationCo.LtdNanjingPeople’s Republic of China

Accurate and timely information on urban vegetation(UV)can be used as an important indicator to estimate the health of *** to the low cost of RGB cameras,true color imagery(TCI)has been widely used for high spatial resolution UV ***,the current index-based and classifier-based UV mapping approaches face problems of the poor ability to accurately distinguish UV and the high reliance on massive annotated samples,*** address this issue,an index-guided semantic segmentation(IGSS)framework is proposed in this ***,a novel cross-scale vegetation index(CSVI)is calculated by the combination of TCI and Sentinel-2 images,and the index value can be used to provide an initial UV ***,reliable UV and non-UV samples are automatically generated for training the semantic segmentation model,and then the refined UV map can be *** experimental results show that the proposed CSVI outperformed the existingfive RGB vegetation indices in highlighting UV cover and suppressing complex backgrounds,and the proposed IGSS workflow achieved satisfactory results with an OA of 87.72%∼88.16%and an F1 score of 87.73%∼88.37%,which is comparable with the fully-supervised method.

关键词： Urban vegetation mapping Sustainable Development Goals(SDGs) cross-scale vegetation index(CSVI) semantic segmentation high-resolution true color imagery(TCI)

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The Echo Model and Simulation Method of Impulse Radar Fuze Based on Attribute Scattering Center Model 3

The Echo Model and Simulation Method of Impulse Radar Fuze B...

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2021 3rd International Conference on Computer, Communications and Mechatronics Engineering, CCME 2021

作者： Kong, Ming Li, Yuzhao Chen, Jichi Wang, Xu Wei, Guohua School of Information and Electronics Beijing Institute of Technology Beijing100081 China Science and Technology on Electromechanical Dynamic Control Laboratory China Beijing Institute of Remote Sensing Equipment Beijing100854 China

This article focuses on solving the problem of performance evaluation and algorithm verification of ultra-bandwidth impulse radar fuze. A modeling and simulation method for the scattering echo of an impulse radar target is proposed. We distinguish the scattering center of the extended target and construct its echo based on the attribute scattering center model. Besides, the position variation characteristics of the scattering center, and the azimuth dependence of the scattering amplitude are fully considered. At the same time, the motion information of the target is modulated to obtain the target scattering echo of the impulse radar. We generate the scattering echo of the typical streamlined moving target at different observation angles by calculation. The changes of the echo position and amplitude of each scattering center are consistent with the scattering echo model. We use the generated echo data to image, and the imaging results are basically consistent with the shape of the target, which verifies the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Bandwidth

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Synthesis of True Color Images from the Fengyun Advanced Geostationary Radiation Imager

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Journal of Meteorological Research 2021年第6期35卷 1136-1147页

作者： Yuchen XIE Xiuzhen HAN Shanyou ZHU School of Remote Sensing&Geomatics Engineering Nanjing University of Information Science&TechnologyNanjing 210044 National Satellite Meteorological Center China Meteorological AdministrationBeijing 100081

The production of true color images requires observational data in the red,green,and blue(RGB)*** Advanced Geostationary Radiation Imager(AGRI)onboard China’s Fengyun-4(FY-4)series of geostationary satellites only has blue and red bands,and we therefore have to synthesize a green band to produce RGB true color *** used random forest regression and conditional generative adversarial networks to train the green band model using Himawari-8 Advanced Himawari Imager *** model was then used to simulate the green channel reflectance of the FY-4 AGRI.A single-scattering radiative transfer model was used to eliminate the contribution of Rayleigh scattering from the atmosphere and a logarithmic enhancement was applied to process the true color *** conditional generative adversarial network model was better than random forest regression for the green band model in terms of statistical significance(e.g.,a higher determination coefficient,peak signal-to-noise ratio,and structural similarity index).The sharpness of the images was significantly improved after applying a correction for Rayleigh scattering,and the images were able to show natural phenomena more *** AGRI true color images could be used to monitor dust storms,forest fires,typhoons,volcanic eruptions,and other natural events.

关键词： Advanced Geostationary Radiation Imager(AGRI) RGB true color random forest regression conditional generative adversarial networks Rayleigh scattering

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Spatial data intelligence and city metaverse: A review

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Fundamental Research 2025年第3期5卷 1169-1193页

作者： Meng, Xiaofeng Li, Yong Liu, Ke Liu, Yu Yang, Bin Song, Xuan Liao, Guoqiong Wang, Senzhang Yu, Ziqiang Chen, Longbiao Pan, Xiao Lin, Yuming School of Information Renmin University of China Beijing 100872 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Information Sciences National Natural Science Foundation of China Beijing 100085 China Institute of Remote Sensing and Geographic Information Systems School of Earth and Space Sciences Peking University Beijing 100871 China School of Data Science and Engineering East China Normal University Shanghai 200062 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 China School of Information Management Jiangxi University of Finance and Economics Nanchang 330013 China School of Computer Science and Engineering Central South University Changsha 410017 China School of Computer and Control Engineering Yantai University Yantai 264005 China Department of Computer Science Xiamen University Xiamen 361000 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang 050043 China

Spatial Data Intelligence (SDI) encompasses acquiring, storing, analyzing, mining, and visualizing spatial data to gain insights into the physical world and uncover valuable knowledge. These understandings and knowledge play a crucial role in connecting physical and virtual realms, such as in developing a City Metaverse (CM) aimed at enhancing and optimizing modern urban environments. The advancement of CM holds immense potential to benefit urban dwellers, making research on SDI an increasingly prominent area of focus. This paper contributes significantly by organizing the relevant research and technologies within a coherent framework. Firstly, we identify SDI technologies capable of collecting real-world information to construct a virtual CM. Subsequently, we delve into the technologies that can be compositely integrated with SDI to facilitate interaction with and management of actual cities from the virtual perspective. Additionally, we emphasize the effectiveness and potential of these methods in practical applications. Lastly, we conclude our survey by discussing emerging challenges associated with technological progress, the industrial chain, legal and regulatory aspects, and ethical and moral considerations. © 2024

关键词： Application prospect City metaverse Smart city Spatial data intelligence Virtual-real interaction

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Geospatial data and web based tools for managing irrigation infrastructure expansion projects

World Water Policy

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World Water Policy 2023年第3期9卷 388-403页

作者： Issac, Annie Maria Harshitha, M.S. Lakshmi, Thatiparthi Vijay Hagare, Dharmappa Maheshwari, Basant Reynolds, Jason K. Rao, Lakshminarayana Tuppad, Pushpa Prasad, Shiva Ynag, Shuqing Sivakumar, Muttucumaru Water Resource Group National Remote Sensing Centre Indian Space Research Organisation Bengaluru India Water Resource Group National Remote Sensing Centre Indian Space Research Organisation Hyderabad India Jawaharlal Nehru Technological University Hyderabad Hyderabad India Western Sydney University Penrith NSW Australia Indian Institute of Science Bengaluru India S.J. College of Engineering JSS Science and Technology University Mysuru India GB Pant University of Agriculture and Technology Udham Singh Nagar India School of Civil Mining and Environmental Engineering University of Wollongong Wollongong Australia

Improving irrigation infrastructure is important for sustaining food security in developing countries like India. The irrigation potential of a system is estimated at the planning phase using conventional approaches. A mismatch in the irrigation potential planned to be created and the land suitable for cultivation can lead to a gap in the irrigation potential created and utilized, making the scheme unrealistic and uneconomical. This study aims to use geospatial data and geographic information system (GIS) tools to identify land suitable for cultivation when planning an irrigation system. This study was conducted in Telangana State in India, where the projected irrigation potential creation was 5 million ha (Mha). It was observed that the total land suitable for cultivation in the state, considering the soil's topography, and physical and chemical characteristics, is about 6 Mha. Time series of average monthly Normalised Difference Vegetation Index (NDVI) over a 10-year period was used to identify the critical blocks. The study illustrates how the geospatial data derived from remote sensing and recent GIS tools can aid managers in planning, implementing, and monitoring irrigation projects. The study also demonstrates how long-term satellite information can be used for regional prioritizationprioritisation for constructing irrigation infrastructure and postconstruction impact assessment. This study was conducted as part of the Young Water Professions (YWP) Training program, which allowed the participants to develop professional competencies and implement their learning in real-world situations. © 2023 The Authors. World Water Policy published by Wiley Periodicals LLC on behalf of Policy Studies Organization.

关键词： GIS tools land suitability for cultivation multicriteria decision analysis YWP training

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