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On-orbit attitude planning for Earth observation imaging with star-based geometric calibration

Science China(Technological Sciences) 2025年第2期68卷 243-256页

作者： Di HUANG Zhifu WANG Jianya GONG Haoran JI Institute of Aerospace Science and Technology School of Remote Sensing and Information EngineeringWuhan UniversityWuhan 430072China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingSchool of Remote Sensing and Information EngineeringWuhan UniversityWuhan 430072China National Key Laboratory of Air-based Information Perception and Fusion Luoyang 471009China Intelligent Game and Decision Laboratory Beijing 100071China

To improve the quality of remote sensing images,a novel on-orbit attitude planning method for Earth observation imaging with star-based geometric calibration is presented in this *** traditional imaging processes,the proposed method includes both pre-calibration and post-calibration stages to enhance the accuracy of satellite geometric positioning.A multiple-constraint attitude planning algorithm was developed to ensure star-based calibration,wherein the satellite camera and star sensors simultaneously capture the images of *** integration of cameras and multiple star sensors enables effective attitude *** the Earth imaging stage,a fast on-orbit attitude planning algorithm was developed to determine the satellite attitude and the optimal imaging times for ground *** with existing methods,the fast on-orbit attitude planning algorithm can significantly reduce computational time and resource consumption via initial value selection and *** demonstrate the effectiveness of the proposed attitude planning method,which was successfully applied to Wuhan 1 satellite.

关键词： attitude planning Earth observation imaging geometric calibration attitude constraints

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A Fast and Robust Calibration Method for Lambert Model in VLP System Without Any Geometric Measurement

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IEEE Internet of Things Journal 2025年第10期12卷 14653-14662页

作者： Huang, Tianming Zhuang, Yuan Yang, Xiansheng Sun, Xiao Yu, Tengfei Cao, Xiaoxiang Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430072 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan Institute of Quantum Technology Shenzhen Research Institute Wuhan430072 China Company Ltd. Suzhou215347 China

Visible light positioning (VLP) is one of the most promising technologies for providing high-precision, low-cost indoor positioning, and navigation services. However, the traditional calibration methods of VLP are complex and unfriendly to users, which hinders the large-scale commercial deployment of VLP systems. In this article, a fast and robust calibration method for received signal strength (RSS)-based VLP system is proposed, which dispenses with geometric measurement and greatly simplifies the calibration procedures. The proposed method calibrates the Lambert model through two steps, first using a ratio method to calibrate the Lambert order, and then estimating the constant term. The actual processes only require moving a robot equipped with a photodetector (PD) along a rectangular trajectory once, and then the program will automatically estimate the required parameters by analyzing the RSS during this period. Experimental results show a good stability of the calibrated parameters, as well as an excellent distance measuring accuracy within 12 cm. The proposed method is 3.7 times more efficient than traditional methods while the positioning accuracy is close, which will greatly reduce the time and labor costs of large-scale deployment. © 2014 IEEE.

关键词： Calibration

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GaoFen-3 Mission for InSAR Time Series Analysis: a Quantitative Evaluation

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IEEE Journal of Selected Topics in applied Earth Observations and remote sensing 2025年 18卷 11428-11450页

作者： Qin, Yuxiao Zhang, Qingjun Hong, Wen Han, Bing Li, Fangfang Shen, Jiaxin Wang, Ke Wang, Mengge Northwestern Polytechnical University School of Electronics and Information Xi'an China China Aerospace Science and Technology Corporation Institute of Remote Sensing Satellite Beijing China Chinese Academy of Sciences Aerospace Information Research Institute Beijing China

GaoFen-3 (GF-3) is China's first civilian high resolution Synthetic Aperture Radar (SAR) satellite mission. Since its launch in 2016, GF-3 has achieved remarkable success in various SAR application fields. However, till this date, research and applications focusing on SAR Interferometry (InSAR) time series analysis have not received enough attention. InSAR time series analysis stands out for its ability to accurately measure surface displacements over a wide range with millimeter-level precision. Currently, there is a notable lack of comprehensive understanding of GF-3's InSAR time series capabilities, untapped potential, and possible areas for improvement. In this study, we conducted a thorough evaluation of GF-3's InSAR time series analysis performance using the persistent scatterer interferometry (PS-InSAR) method. For the first time, we processed 25 GF-3 images taken between 2020 and 2023 using the PSInSAR method. Our results demonstrated that GF-3 is capable of producing high-quality PS-InSAR outcomes. When compared to Sentinel-1 (S-1), the root mean square error of the estimated velocity between GF-3 and Sentinel-1 was 2 mm/yr. The measured point density delivered by GF-3 amounted to 7610/km2 20, substantially the same as TerraSAR-X and 50% higher than the highest standard set by the European Ground Motion Service (EGMS). At last, despite GF-3's spatial baseline variation exceeding 660 m (1σ), since the persistent scatterers hardly decorrelate, the PS-InSAR performance quality is virtually unaffected. This pioneering study conclusively demonstrated that GF-3 is capable of delivering high-quality PS-InSAR results. With the 3-satellites GF-3 constellation now in orbit, our work will provide vital support and lay a solid foundation for operational PS-InSAR applications using domestic satellites. Furthermore, we anticipate that the promising results obtained from this work will facilitate substantial progress in China's InSAR related research and operational appli

关键词： Mean square error

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PID-CD: PID Control is What Change Detection Needs

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IEEE Journal of Selected Topics in applied Earth Observations and remote sensing 2025年 18卷 10388-10402页

作者： Ye, Zhiwei Sheng, Shirui He, Qiyi Wang, Mingwei Xu, Chuan Song, Zhina Fang, Ziqian Mei, Liye Yan, Lingyu Lai, Xudong Hubei University of Technology School of Computer Science Wuhan430068 China Wuhan University School of Remote Sensing and Information Engineering Institute of Development Strategy and Planning Wuhan430079 China

remote sensing change detection (RSCD) is a remote sensing technique used to identify geomorphological changes in bitemporal images of the same region. However, the identification of change regions is often hindered by false changes in similar objects, resulting in inaccurate boundary recognition of the change regions. This article adopts the proportional-integral-derivative (PID) control concept and designs two modules: PID feature alignment (PIDFA) and PID feature fusion (PIDFF), which makes up the PID-CD model. The PIDFA enhances and corrects the change region features using manifold structures and adaptive strategies, while the PIDFF introduces a third branch to guide multilevel feature fusion, progressively refining the boundaries of the change regions. Finally, extensive experiments on benchmark datasets, including LEVIR-CD, S2Looking, and CL-CD, display that PID-CD achieves state-of-the-art performance in RSCD. Specifically, PID-CD achieves the F1 score of 91.94% on LEVIR-CD, 66.61% on S2Looking, and 74.11% on CL-CD, showing significant improvements in detail and boundary of change area. © 2008-2012 IEEE.

关键词： Geomorphology

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Development and Challenges of Hyperspectral Image Classification Techniques

Development and Challenges of Hyperspectral Image Classifica...

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2024 Advanced Fiber Laser Conference, AFL 2024

作者： Wang, Pengyu Cheng, Haobo Gao, Kun Zhang, Xiaodian Li, Wei School of Optics and Photonics Beijing Institute of Technology China Institute of Remote Sensing Satellite China Academy of Space Technology China School of Information and Electronics Beijing Institute of Technology China

ISBN: (数字)9781510688889

ISBN: (纸本)9781510688872

Hyperspectral image classification is a pivotal task in remote sensing, leveraging the rich spatial and spectral information contained in hyperspectral images. This paper addresses the challenges inherent in hyperspectral classification, including spectral variability, band redundancy, and data scarcity. We delineate the relationship between hyperspectral classification, semantic segmentation, and target recognition, categorizing classifiers into spectral and spatial-spectral feature types. Spectral feature classifiers, ranging from traditional statistical methods to deep learning approaches, offer varying levels of performance and computational efficiency. Spatial-spectral feature classifiers, integrating spatial information, enhance classification accuracy by addressing spectral variability and noise. We discuss the strengths and limitations of different methods, highlighting the potential of deep learning-based approaches and the importance of joint spatial-spectral feature extraction. Future research should focus on overcoming the challenges associated with data acquisition, feature engineering, and model interpretability to advance hyperspectral image classification applications. © 2025 SPIE.

关键词： Semantic Segmentation

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Simulations of GaoFen-5 Directional Polarimetric Camera(DPC) Observations Using the Advanced Vector Discrete Ordinate Radiative Transfer Model

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Advances in Atmospheric Sciences 2025年第3期42卷 486-500页

作者： Yuchen XIE Ziqiang ZHU Fuzhong WENG Zhengqiang LI Xiuzhen HAN School of Remote Sensing&Geomatics Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Earth System Modeling and Prediction Centre China Meteorological AdministrationBeijing 100081China University of Chinese Academy of Sciences Beijing 100049China Environmental Protection Key Laboratory of Satellite Remote Sensing&Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research InstituteChinese Academy of Sciences.Beijing 100101China National Satellite Meteorological Center China Meteorological AdministrationBeijing 100081China

In recent years,polarization remote sensing has garnered increasing attention,particularly within the realm of *** accurately simulate polarization information,the vector discrete-ordinate radiative transfer(VDISORT)model developed earlier by the community is further enhanced to an advanced version(referred to as A-VDISORT)through an improved ocean surface *** Fresnel reflection matrix,which includes wind-generated roughness and shading effects,is served by an ocean bidirectional reflection distribution function(BRDF).The simulation from AVDISORT is compared with SCIATRAN for a Rayleigh scattering atmosphere,and the influence of water-leaving radiance is analyzed by the PSTAR(Polarized System for Transfer of Atmospheric Radiation) *** GaoFen-5 Directional Polarimetric Camera(DPC) observations with polarization and multi-angle information,clear-sky pixel recognition over the ocean is first carried *** DPC reflectance of clear conditions is normalized and compared with the *** is shown that A-VDISORT has a high simulation accuracy with a bias of –*** difference between simulation and observation exhibits a standard normal probability distribution function.

关键词： vector radiative transfer equation A-VDISORT ocean BRDF GF-5 DPC

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Complete the exploration of low-resource knowledge graph completion based on large model technology 24

Complete the exploration of low-resource knowledge graph com...

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3rd International Conference on Artificial Intelligence and Intelligent information Processing, AIIIP 2024

作者： Zhao, Pei Zhang, Longxing Zhao, Jiawen Xi’an Aerospace Tianhui Data Technology Co. Ltd. Shanxi Xi’an China Beijing Institute of Remote Sensing Information Beijing China

ISBN: (纸本)9798400707308

In the construction and application of knowledge graph, it is a realistic research problem to complete the knowledge in the low resource field. Traditional methods that rely on manual annotation and rules are not only costly, but also have limitations in coverage and scalability. To solve this problem, this paper proposes a large model technique, combining fine-tuning and knowledge transfer strategies. Firstly, to improve the ability of fine-tuning of the large model, the rich knowledge learned by the large model in the high resource field to assist the completion of the low resource knowledge graph through knowledge transfer technology to make up for the shortage of direct extraction. The experimental results show that this method can effectively improve the completion rate and accuracy of the knowledge graph, especially in the completion of entity relations and attribute filling. Furthermore, we explore the impact of different fine-tuning strategies and knowledge transfer methods on the completion effect, providing experimental empirical and theoretical support for future studies on similar issues. © 2024 Copyright held by the owner/author(s).

关键词： Knowledge graph

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Large-scale synoptic and weather drivers for the co-occurrence of fine particulate matter and ozone extremes over China

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Science China Earth Sciences 2025年第5期68卷 1448-1457页

作者： Wei WANG Yiting ZHOU Mengmeng LI Yuting LU Xinyi JIANG Tijian WANG Bingliang ZHUANG Shu LI Chenchao ZHAN Xiaoyun SUN Xin HUANG School of Atmospheric Sciences Nanjing University Nanjing-Helsinki Institute in Atmospheric and Earth System Sciences Nanjing University Frontiers Science Center for Critical Earth Material Cycling School of Atmospheric Physics Nanjing University of Information Science and Technology Anhui Province Key Laboratory of Atmospheric Science and Satellite Remote Sensing Anhui Institute of Meteorological Sciences

The co-occurrence of extreme weather events and air pollution events, such as elevated fine particulate matter(PM2.5)and ozone（O3）, poses a significant threat to human health, a concern that is anticipated to be exacerbated under climate *** a 9-year dataset of surface air pollutant observations and reanalysis data, this study investigated the large-scale synoptic and weather drivers behind the co-occurrence of PM2.5and O3extremes in China. These co-occurrences are mostly observed during the warm season（March to October） and are particularly prevalent(25.8 days yr-1) in cleaner regions situated in South China. Such co-occurrences are usually associated with elevated air temperatures（～27.7°C）, lower wind speeds(～1.4 ms-1) and atmospheric humidity（～51.9%）. Notably, a substantial proportion of these co-occurrences（up to 76.4%） coincide with extreme weather linked to stagnant weather and extreme heat conditions. By employing an objective clustering approach, we identified the typical synoptic systems conducive to the widespread co-occurrence of PM2.5and O3extremes, including the Western Pacific Subtropical High, Northeast Blocking High, Mongolian High, and typhoons. These findings underscore a robust correlation between the co-occurrence of PM2.5and O3extremes and the annual variations in extreme weather and prevailing weather patterns, suggesting an increased likelihood of concurrent air pollution and extreme weather episodes under a warming climate across China.

关键词： Co-occurrence pollution Ozone PM2.5 Extreme weather T-PCA

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Urban Tree Canopy Mapping and Analysis Using Iterative Annotation Method and Deep Learning: A Case Study in Beijing

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IEEE Journal of Selected Topics in applied Earth Observations and remote sensing 2025年 18卷 12645-12659页

作者： Ding, Yulong Cui, Ximin Chen, Zhengchao Wang, Zeqing Yuan, Debao Meng, Xiang Yang, Xuan Xu, Yue Tian, Xiangyu China University of Mining and Technology-Beijing College of Geoscience and Surveying Engineering Beijing100083 China Chinese Academy of Sciences Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research Institute Beijing100094 China China Aerospace Academy of Systems Science and Engineering Beijing100037 China Chinese Academy of Sciences China Remote Sensing Satellite Ground Station Aerospace Information Research Institute Beijing100094 China Chinese Academy of Sciences Institute of Geographic Sciences and Natural Resources Research Beijing100094 China

Urban trees have significant ecological and social functions, and generating Urban Tree Canopy (UTC) maps is an effective method for understanding their distribution. However, existing studies primarily rely on medium- to low-resolution imagery for large-scale extraction or high-resolution imagery for small-scale extraction, making it challenging to balance spatial coverage and accuracy. To comprehensively analyze the tree canopy characteristics of urban trees across large areas, this study selects Beijing as the research area, and employs highresolution remote sensing imagery with deep learning techniques to construct the UTC map of Beijing. Deep learning models typically require large amounts of labeled data for training, but manually annotating pixel-level labels is time-consuming and costly. To address this, we propose an iterative method based on Intersection over Union (IoU), integrating multi-scale segmentation and nearest-neighbor classification algorithms. By leveraging a limited number of labeled pixels, this approach efficiently generates a extensive labeled data, enabling the rapid construction of a high-precision dataset. Using this method, we established the Beijing Urban Tree Canopy Dataset (BUTCD). By Employing BUTCD and InternImage_T+UPerNet as the tree canopy segmentation network, we generated the urban tree canopy map for Beijing. Accuracy assessments demonstrated an accuracy of 0.9806, precision of 0.9569, recall of 0.9702, mean IoU (mIoU) of 0.9296, and F1-score of 0.9635. We analyzed the results of Beijing's urban tree canopy, revealing that the tree canopy coverage rate is approximately 43.99%, with higher coverage in the northwest and lower coverage in the southeast. The per capita tree canopy area in Beijing was calculated as 330.29 m². Within Beijing's central urban area, trees outside forests account for 26.54% of the the total urban tree population, indicating that conventional statistics on tree-related data in this area tend to underest

关键词： Vegetation Urban areas Deep learning Accuracy Feature extraction Data mining Image segmentation Forests Land surface Biological system modeling

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MVCL: Multi-View Complementary Learning Network for remote sensing Image Change Detection

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IEEE Journal of Selected Topics in applied Earth Observations and remote sensing 2025年 18卷 12831-12844页

作者： Huang, Andong Xu, Chuan Mei, Liye Ye, Zhiwei Yang, Wei Wang, Ying Li, Xinghua Hubei University of Technology School of Computer Science Wuhan430068 China Wuhan University Institute of Technological Sciences Wuhan430072 China Wuchang Shouyi University School of Information Science and Engineering Wuhan430032 China Wuhan University School of Remote Sensing Information Engineering Wuhan430072 China

remote sensing (RS) image change detection (CD) aims to identify and localize the differences in the same geographical area observed across bi-temporal RS images. Existing CD methods typically focus on characterizing and distinguishing change regions, while often overlooking the non-change areas (background information) in RS images. Therefore, the influence of subtle changes in non-change regions may be amplified, leading to false negatives or false positives in the change regions. To address this issue, we propose a multi-view complementary learning network (MVCL) for RS image CD, which considers change detection from both background view and change view perspectives, while focusing on the extraction and aggregation of both change and non-change (background) features. Specifically, we introduce a background branch during the encoder stage dedicated to extracting background information. This background information is then injected into the change regions through the background-dependent feature fusion module (BFFM), serving as prior knowledge. By leveraging the stable background information, irrelevant and noisy changes are suppressed, guiding the generation of change regions and reducing the interference of background noise. Furthermore, to mitigate the noise introduced during multi-scale fusion in the non-change (background) areas, we propose a prior-guided spatial background enhancement module (PGBM), which consists of a prior semantic guidance strategy and a spatial background enhancement module. According to the results of experiments conducted on three public datasets, the proposed method outperforms several SOTA methods in both quantitative metrics and visual effectiveness. The source code is available at https://***/AdKingH/MVCL. © 2025 IEEE.

关键词： Change detection

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