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Star extraction by star sensors for daytime images affected by atmospheric turbulence

Chinese Journal of Aeronautics 2025年

作者： Wanxiang GOU Yinhu ZHAN Chonghui LI Shuai TONG Yong ZHENG Yuan YANG Hanxu LI College of Geospatial Information Information Engineering University Zhengzhou 450000 China Key Laboratory of Smart Earth Beijing 100029 China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural Resources Zhengzhou 450000 China Collaborative Innovation Center of BDS Navigation Application Technology Henan Province Zhengzhou 450000 China

Daytime star images captured by dedicated near-space star sensors are characterized by short exposures, high noise, and low Signal-to-Noise Ratios (SNRs). Such imaging is also affected by atmospheric turbulence, causing optical phenomena, such as scintillation, distortion, and jitter. This causes difficulty in recording high-precision star images during the daytime. This study proposes an adaptive star point extraction method based on dynamically predicting stars’ positions. First, it predicts the approximate position of stars based on the star catalog, sensor attitude, observation time, and other information, improving the extraction accuracy. Second, it employs a regional SNR sorting method that adaptively selects star images with higher SNRs, suppressing the scintillation effect and enhancing the SNR of star images. Third, depending on the star’s motion trajectory characteristics on the image plane, it utilizes the centroid smoothing method for extraction, thus overcoming the impact of star drift. Field experiments demonstrate that the proposed method can effectively overcome star scintillation, drift, and irregular imaging caused by atmospheric turbulence, achieving a 100% success rate. Moreover, the extraction accuracy improves by more than 80% compared to traditional adaptive methods, attaining a value of 0.05 pixels (0.5’’), thereby meeting the requirements of daytime astronomical attitude determination and positioning.

关键词： Astronomical navigation Star sensor Daytime star image Atmospheric turbulence Star extraction Centroid accuracy

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Planar near-field to far-field transformation for massive MIMO systems 34

Planar near-field to far-field transformation for massive MI...

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第三十四届中国控制会议

作者： HAN Xiangzi TAN Xiaobin WU Fan CAS Key laboratory of Technology in Geo-spatial Information Processing and Application System School of Information Science and Technology University of Science and Technology of China

ISBN: (纸本)9781467374439

The massive or large scaled multiple input multiple output(MIMO) systems have gained huge consideration due to high achievable data rates, reliable system performance and advantaged energy efficiency for future wireless networks. For such systems, we should have a keen knowledge of the channel model, the ways of configuration, spectral efficiency and so on. The obtaining of the far-field pattern in the massive MIMO system from measurements made in the near-field has attracted widespread attention recently. In this article, two basic techniques for accomplishing this have been proposed. In the first technique, the NTFF(near-field to far-field) transformation is based on the equivalence principle. While in the second technique, it is based on the plane wave spectrum(PWS) expansion. Both of the methods have their own advantages for the massive MIMO systems. One can drastically reduce the computation time and the other can make enormous reduction in the storage consumption. Experimental results show that the two transformation techniques are proved to be feasible and they are consistent with each other.

关键词： massive MIMO,NTFF transformation,the equivalence principle,PWS expansion

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Attributing climate and weather extremes to Northern Hemisphere sea ice and terrestrial snow: progress, challenges and ways forward

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npj Climate and Atmospheric Science 2025年第1期8卷 1-22页

作者： Ye, Kunhui Cohen, Judah Chen, Hans W. Zhang, Shiyue Luo, Dehai Hamouda, Mostafa Essam Atmospheric Oceanic and Planetary Physics University of Oxford Oxford United Kingdom Atmospheric and Environmental Research Lexington United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge United States Department of Space Earth and Environment Chalmers University of Technology Gothenburg Sweden Key Laboratory of Meteorological Disaster Ministry of Education Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Joint International Research Laboratory of Climate and Environment Change Nanjing University of Information Science and Technology Nanjing China Key Laboratory of Earth System Numerical Modeling and Application and Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China and University of Chinese Academy of Sciences Beijing China Astronomy and Meteorology Department Faculty of Science Cairo University Cairo Al Giza Egypt

Sea ice and snow are crucial components of the cryosphere and the climate system. Both sea ice and spring snow in the Northern Hemisphere (NH) have been decreasing at an alarming rate in a changing climate. Changes in NH sea ice and snow have been linked with a variety of climate and weather extremes including cold spells, heatwaves, droughts and wildfires. Understanding of these linkages will benefit the predictions of climate and weather extremes. However, existing work on this has been largely fragmented and is subject to large uncertainties in physical pathways and methodologies. This has prevented further substantial progress in attributing climate and weather extremes to sea ice and snow change, and will potentially risk the loss of a critical window for effective climate change mitigation. In this review, we synthesize the current progress in attributing climate and weather extremes to sea ice and snow change by evaluating the observed linkages, their physical pathways and uncertainties in these pathways, and suggesting ways forward for future research efforts. By adopting the same framework for both sea ice and snow, we highlight their combined influence and the cryospheric feedback to the climate system. We suggest that future research will benefit from improving observational networks, addressing the causality and complexity of the linkages using multiple lines of evidence, adopting large-ensemble approaches and artificial intelligence, achieving synergy between different methodologies/disciplines, widening the context, and coordinated international collaboration. © The Author(s) 2025.

关键词： Atmospheric dynamics Climate change Cryospheric science

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Research and application of Automatic Classification Method for Patrol Targets of Transmission Lines Based on LiDAR Point Cloud 6th

Research and Application of Automatic Classification Method ...

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6th International Conference on Geo-informatics in Sustainable Ecosystem and Society, GSES 2018

作者： Zhang, Chen Li, Yong Zhou, Xia Yang, Ji Yang, Chuanxun Liu, Hongkai Guangzhou Institute of Geography Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou510070 China

ISBN: (纸本)9789811370243

The development of airborne laser radar technology and cloud data processing technology provides a new technical means for the acquisition and processing of geospatial 3D data. Based on the self-developed LiDAR UAV, this paper proposes a new point cloud automatic classification method (TL-PCACM) of transmission lines by using the improved fast 3D convex hull construction algorithm, k-means clustering analysis algorithm and Region-growing algorithm, which have high classification accuracy, good classification effect and fast processing speed. The accuracy of point cloud automatic classification is over 95%, the efficiency can reach 60 km/h. The proposed method is applied to the analysis of tree obstacles under transmission lines. The efficiency of the automatic identification of tree obstacles can reach 720 towers/hour. The accuracy can reach 50 cm. It provides high efficiency, high quality, fully automatic and intelligent data processing method for the classification of tree obstacles, effectively improving the processing accuracy and speed of geospatial 3D information data. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Non-negative matrix factorization based unmixing for principal component transformed hyperspectral data

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Frontiers of information technology & Electronic Engineering 2016年第5期17卷 403-412页

作者： Xiu-rui GENG Lu-yan JI Kang SUN Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China MOE Key Laboratory for Earth System Modeling Center for Earth System Science Tsinghua University Beijing 100084 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang 050000 China

Non-negative matrix factorization(NMF) has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings:(1) since the dimensionality of hyperspectral data is usually very large, NMF tends to suffer from large computational complexity for the popular multiplicative iteration rule;(2) NMF is sensitive to noise(outliers), and thus the corrupted data will make the results of NMF meaningless. Although principal component analysis(PCA) can be used to mitigate these two problems, the transformed data will contain negative numbers, hindering the direct use of the multiplicative iteration rule of NMF. In this paper, we analyze the impact of PCA on NMF, and find that multiplicative NMF can also be applicable to data after principal component transformation. Based on this conclusion, we present a method to perform NMF in the principal component space, named ‘principal component NMF'(PCNMF). Experimental results show that PCNMF is both accurate and time-saving.

关键词： Non-negative matrix factorization(NMF) Principal component analysis(PCA) Endmember Hyperspectral

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Spatiotemporal analysis of COVID-19 risk in Guangdong Province based on population migration

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Journal of Geographical Sciences 2020年第12期30卷 1985-2001页

作者： YE Yuyao WANG Changjian ZHANG Hong'ou YANG Ji LIU Zhengqian WU Kangmin DENG Yingbin Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangdong Academy of SciencesGuangzhou 510070China Southern Marine Science and Engineering Guangdong Laboratory Guangzhou 511458China School of Architecture and Urban Planning Guangdong University of TechnologyGuangzhou 510090China

Population migration,especially population inflow from epidemic areas,is a key source of the risk related to the coronavirus disease 2019(COVID-19)*** paper selects Guangdong Province,China,for a case *** utilizes big data on population migration and the geospatial analysis technique to develop a model to achieve spatiotemporal analysis of COVID-19 *** model takes into consideration the risk differential between the source cities of population migration as well as the heterogeneity in the socioeconomic characteristics of the destination cities of population *** further incorporates a time-lag process based on the time distribution of the onset of the imported *** theory,the model will be able to predict the evolutional trend and spatial distribution of the COVID-19 risk for a certain time period in the future and provide support for advanced planning and targeted prevention *** research findings indicate the following:(1)The COVID-19 epidemic in Guangdong Province reached a turning point on January 29,2020,after which it showed a gradual decreasing trend.(2)Based on the time-lag analysis of the onset of the imported cases,it is common fora time interval to exist between case importation and illness onset,and the proportion of the cases with an interval of 1-14 days is relatively high.(3)There is evident spatial heterogeneity in the epidemic risk;the risk varies significantly between different areas based on their imported risk,susceptibility risk,and ability to prevent the spread.(4)The degree of connectedness and the scale of population migration between Guangdong’s prefecture-level cities and their counterparts in the source regions of the epidemic,as well as the transportation and location factors of the cities in Guangdong,have a significant impact on the risk classification of the cities in *** first-tier cities-Shenzhen and Guangzhou-are high-risk *** cities in the Pearl River Delta that are adjacent

关键词： population migration COVID-19 epidemic risk time-lag process spatiotemporal analysis

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A Comparative Study of Single-polarized and Polarimetric SAR Images Applied on Unsupervised Classification

A Comparative Study of Single-polarized and Polarimetric SAR...

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2021 CIE International Conference on Radar, Radar 2021

作者： Qu, Junrong Qiu, Xiaolan Key Laboratory of Technology in Geospatial Information and Application System Aerospace Information Research Institute School of Electronic Electrical and Communication Engineering Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665498142

SAR image understanding is a significant but also challenging issue in practice. In order to detect the difference between single-polarized and polarimetric SAR (PolSAR) data and explore more information from the pre one, we use GF3 data for experiments and adopt the Deep Clustering with Convolutional Autoencoders (DCEC) algorithm for single-polarized SAR image unsupervised classification. Using both the Kappa coefficient and mutual information derived from the confusion matrix, the results of single-polarized SAR data and PolSAR data are compared. It shows that the classification results of single-polarized SAR data match with the polarimetric ones to some extent, and their results are comparable in some specific applications. This paper presents the potential of information mining from single-polarized SAR images, as well as gives some references for the selection and trade-off between single- and full-polarized SAR data. © 2021 IEEE.

关键词： comparison polarimetric SAR single-polarized SAR unsupervised classification

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Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery

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Frontiers of information technology & Electronic Engineering 2019年第2期20卷 253-264页

作者： Ting-ting JIN Xiao-qiang SHE Xiao-lan QIU Bin LEI Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of ElectronicsChinese Academy of Sciences University of Chinese Academy of Sciences Huawei Software Technologies Co. Ltd.

Classification of intertidal area in synthetic aperture radar(SAR) images is an important yet challenging issue when considering the complicatedly and dramatically changing features of tidal fluctuation. The difficulty of intertidal area classification is compounded because a high proportion of this area is frequently flooded by water, making statistical modeling methods with spatial contextual information often ineffective. Because polarimetric entropy and anisotropy play significant roles in characterizing intertidal areas, in this paper we propose a novel unsupervised contextual classification algorithm. The key point of the method is to combine the generalized extreme value(GEV) statistical model of the polarization features and the Markov random field(MRF) for contextual smoothing. A goodness-of-fit test is added to determine the significance of the components of the statistical model. The final classification results are obtained by effectively combining the results of polarimetric entropy and anisotropy. Experimental results of the polarimetric data obtained by the Chinese Gaofen-3 SAR satellite demonstrate the feasibility and superiority of the proposed classification algorithm.

关键词： Intertidal classification Polarimetric synthetic aperture radar Finite mixture model Markov random field Generalized extreme value model

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Vegetation feedbacks accelerated the late Miocene climate transition

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Science Advances 2025年第18期11卷 eads4268页

作者： Zhang, Ran Guo, Jiaqi Bradshaw, Catherine D. Xu, Xiyan Shen, Tiantian Li, Shufeng Nie, Junsheng Zhang, Chunxia Li, Xiangyu Liu, Ze Zhang, Jian Jiang, Dabang Hu, Yongyun Sun, Jimin State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Nanjing University of Information Science and Technology Nanjing China Department of Atmosphere and Ocean Sciences School of Physics Peking University Beijing China The Global Systems Institute University of Exeter Exeter United Kingdom Met Office Hadley Centre Exeter United Kingdom College of Earth and Environmental Science Lanzhou University Lanzhou China CAS Key Laboratory of Tropical Forest Ecology Xishuangbanna Tropical Botanical Garden Chinese Academy of Sciences Mengla China Institute of Geology and Geophysics Chinese Academy of Sciences Beijing China Department of Atmospheric Science School of Environmental Studies China University of Geosciences Wuhan China Frontiers Science Center for Deep Ocean Multispheres and Earth System Key Lab of Submarine Geosciences and Prospecting Techniques MOE and College of Marine Geosciences Ocean University of China Qingdao China University of Chinese Academy of Sciences Beijing China

The late Miocene was an important stage for the formation of modern-like ecological and environmental patterns. Proxy data from the middle to late Miocene reveal that large-scale cooling and drying occurred;however, the reasons for this climate transition remain unclear. Through a compilation of proxy data and climate simulations, our results indicate that atmospheric CO2 decline markedly decreased the temperature and reduced the precipitation in most of the land area, while the paleogeographic changes enhanced cooling at northern high latitudes and increased precipitation in East Asia, East Africa, and South America. In comparison, vegetation changes accelerated cooling at northern high latitudes (the maximum cooling exceeded 10°C) and modulated precipitation at low- and mid-latitude continents (the maximum decrease was close to 30%). This deepens the understanding of the mechanism of the late Miocene climate transition and highlights the importance of vegetation feedbacks during global climate change. Copyright © 2025 The Authors, some rights reserved.

关键词： Global warming

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High-precision inversion of vegetation parameters in the AI era: Integrating hyperspectral remote sensing and deep learning

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Innovation 2025年

作者： Hu, Jinkang Peng, Dailiang Chen, Jing M. Huete, Alfredo R. Yu, Le Lou, Zihang Cheng, Enhui Yang, Xuan Zhang, Bing Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China College of Resource and Environment University of Chinese Academy of Sciences Beijing100094 China School of Geographical Sciences Fujian Normal University Fuzhou350117 China Department of Geography and Planning University of Toronto TorontoONM5S 3G3 Canada School of Life Sciences University of Technology Sydney UltimoNSW2007 Australia Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing100084 China Institute of Agricultural Remote Sensing and Information Technology Application College of Environmental and Resource Sciences Zhejiang University Hangzhou310058 China China Remote Sensing Satellite Ground Station Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

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